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General matrix multiplication (gemm) is a fundamental kernel in scientific computing and current frameworks for deep learning. Modern realisations of gemm are mostly written in C, on top of a small, highly tuned micro-kernel that is usually encoded in assembly. The high performance realisation of gemm in linear algebra libraries in general include a single micro-kernel per architecture, usually implemented by an expert. In this paper, we explore a couple of paths to automatically generate gemm micro-kernels, either using C++ templates with vector intrinsics or high-level Python scripts that directly produce assembly code. Both solutions can integrate high performance software techniques, such as loop unrolling and software pipelining, accommodate any data type, and easily generate micro-kernels of any requested dimension. The performance of this solution is tested on three ARM-based cores and compared with state-of-the-art libraries for these processors: BLIS, OpenBLAS and ArmPL. The experimental results show that the auto-generation approach is highly competitive, mainly due to the possibility of adapting the micro-kernel to the problem dimensions. [ABSTRACT FROM AUTHOR]

With the continuous progress of technology, the subject of life science plays an increasingly important role, among which the application of artificial intelligence in the medical field has attracted more and more attention. Bell facial palsy, a neurological ailment characterized by facial muscle weakness or paralysis, exerts a profound impact on patients' facial expressions and masticatory abilities, thereby inflicting considerable distress upon their overall quality of life and mental well-being. In this study, we designed a facial attribute recognition model specifically for individuals with Bell's facial palsy. The model utilizes an enhanced SSD network and scientific computing to perform a graded assessment of the patients' condition. By replacing the VGG network with a more efficient backbone, we improved the model's accuracy and significantly reduced its computational burden. The results show that the improved SSD network has an average precision of 87.9% in the classification of light, middle and severe facial palsy, and effectively performs the classification of patients with facial palsy, where scientific calculations also increase the precision of the classification. This is also one of the most significant contributions of this article, which provides intelligent means and objective data for future research on intelligent diagnosis and treatment as well as progressive rehabilitation. [ABSTRACT FROM AUTHOR]

In scientific computing, neural networks have been widely used to solve partial differential equations (PDEs). In this paper, we propose a novel RBF-assisted hybrid neural network for approximating solutions to PDEs. Inspired by the tendency of physics-informed neural networks (PINNs) to become local approximations after training, the proposed method utilizes a radial basis function (RBF) to provide the normalization and localization properties to the input data. The objective of this strategy is to assist the network in solving PDEs more effectively. During the RBF-assisted processing part, the method selects the center points and collocation points separately to effectively manage data size and computational complexity. Subsequently, the RBF processed data are put into the network for predicting the solutions to PDEs. Finally, a series of experiments are conducted to evaluate the novel method. The numerical results confirm that the proposed method can accelerate the convergence speed of the loss function and improve predictive accuracy. [ABSTRACT FROM AUTHOR]

Hier wird die erfolgreiche kristallisationsinduzierte dynamische Racematspaltung (CIDR) eines α‐lithiierten Phosphinoborans mithilfe des leicht zugänglichen und kostengünstigen chiralen Liganden (R,R)‐TMCDA beschrieben. Ausgehend von dem wichtigen P‐prochiralen Baustein Dimethylphenylphosphinoboran konnten Phosphinoborane in Ausbeuten von bis zu 80 % und Enantiomerenverhältnissen von bis zu 98 : 2 durch Kristallisation des lithiierten Zwischenprodukts erhalten werden. NMR‐basierte Deuterium‐Markierungsexperimente zeigen, dass die Epimerisierung in Lösung auf dem intermolekularen Protonentransfer zwischen dem nicht lithiierten Phosphinoboran und dem entsprechenden lithiierten Zwischenprodukt beruht, sodass die Anwesenheit überschüssigen Eduktes in einem optimierten Lösungsmittelgemisch der Hauptfaktor für eine erfolgreiche enantioselektive Synthese ist. Quantenchemische Berechnungen mit verschiedenen auf Festkörperstrukturen basierenden Modellsystemen bestätigen diese experimentellen Ergebnisse. Durch den Einblick in den Epimerisierungsmechanismus wurden wesentliche Prinzipien für die CIDR von lithiierten Phosphinoboranen aufgeklärt, die auf andere wichtige P‐stereogene Verbindungen und einfache chirale Amine erweitert werden könnten. [ABSTRACT FROM AUTHOR]

With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring millions of CPU hours to perform. One solution is multi-fidelity emulation, which uses data of different fidelities to train an efficient predictive model which emulates the expensive simulator. For complex scientific problems and with careful elicitation from scientists, such multi-fidelity data may often be linked by a directed acyclic graph (DAG) representing its scientific model dependencies. We thus propose a new Graphical Multi-fidelity Gaussian Process (GMGP) model, which embeds this DAG structure (capturing scientific dependencies) within a Gaussian process framework. We show that the GMGP has desirable modeling traits via two Markov properties, and admits a scalable algorithm for recursive computation of the posterior mean and variance along at each depth level of the DAG. We also present a novel experimental design methodology over the DAG given an experimental budget, and propose a nonlinear extension of the GMGP via deep Gaussian processes. The advantages of the GMGP are then demonstrated via a suite of numerical experiments and an application to emulation of heavy-ion collisions, which can be used to study the conditions of matter in the Universe shortly after the Big Bang. The proposed model has broader uses in data fusion applications with graphical structure, which we further discuss. [ABSTRACT FROM AUTHOR]

Metaphors that compare the computer to a human brain are common in computer science and can be traced back to a fertile period of research that unfolded after the Second World War. To conceptualize the emerging "intelligent" properties of computing machines, researchers of the era created a series of virtual objects that served as interpretive devices for representing the immaterial functions of the computer. This paper analyses the use of the terms "artificial" and "virtual" in scientific papers, textbooks, and popular articles of the time, and examines how, together, they shaped models in computer science used to conceptualize computer processes. [ABSTRACT FROM AUTHOR]

The relationship between philosophy and science has always been complementary. Today, while science moves increasingly fast and philosophy shows some problems in catching up with it, it is not always possible to ignore such relationships, especially in some disciplines such as philosophy of mind, cognitive science, and neuroscience. However, the methodological procedures used to analyze these data are based on principles and assumptions that require a profound dialogue between philosophy and science. Following these ideas, this work aims to raise the problems that a classical connectionist theory can cause and problematize them in a cognitive framework, considering both philosophy and cognitive sciences but also the disciplines that are near to them, such as AI, computer sciences, and linguistics. For this reason, we embarked on an analysis of both the computational and theoretical problems that connectionism currently has. The second aim of this work is to advocate for collaboration between neuroscience and philosophy of mind because the promotion of deeper multidisciplinarity seems necessary in order to solve connectionism's problems. In fact, we believe that the problems that we detected can be solved by a thorough investigation at both a theoretical and an empirical level, and they do not represent an impasse but rather a starting point from which connectionism should learn and be updated while keeping its original and profoundly convincing core. [ABSTRACT FROM AUTHOR]

Matrices with low-rank structure are ubiquitous in scientific computing. Choosing an appropriate rank is a key step in many computational algorithms that exploit low-rank structure. However, estimating the rank has been done largely in an ad-hoc fashion in large-scale settings. In this work we develop a randomized algorithm for estimating the numerical rank of a (numerically low-rank) matrix. The algorithm is based on sketching the matrix with random matrices from both left and right; the key fact is that with high probability, the sketches preserve the orders of magnitude of the leading singular values. We prove a result on the accuracy of the sketched singular values and show that gaps in the spectrum are detected. For an m × n (m ≥ n) matrix of numerical rank r , the algorithm runs with complexity O (m n log ⁡ n + r 3) , or less for structured matrices. The steps in the algorithm are required as a part of many low-rank algorithms, so the additional work required to estimate the rank can be even smaller in practice. Numerical experiments illustrate the speed and robustness of our rank estimator. [ABSTRACT FROM AUTHOR]

Digital computers were invented in the 1940s. They are sophisticated and versatile machines whose functioning is grounded in elaborate theory. Advances in theory and the availability of computers helped computer science to develop as an academic discipline, and university departments for the same started coming up in the 1960s. Computer science covers all phenomenon related to computers and consists primarily of man-made laws governing building, programming, and using computers. Computational thinking is a way of thinking influenced by computers and computer science. There are two schools of thought on computational thinking. The first school sees computational thinking as the use of computers to explore the world, while the other sees computational thinking as the application of concepts from computer science to solve real-world problems. Scholars typically agree that computational thinking has four essential components, viz., abstraction, decomposition, algorithm design, and generalization. Computational thinking is often feted by computer scientists as a useful skill that can be used by anybody anywhere. However, it is necessary to find out ways for successfully using computational thinking in domains other than computer science before it can be declared a universal skill. [ABSTRACT FROM AUTHOR]

Purpose: The present study is about generating metadata to enhance thematic transparency and facilitate research on interview collections at the Research Documentation Centre, Centre for Social Sciences (TK KDK) in Budapest. It explores the use of artificial intelligence (AI) in producing, managing and processing social science data and its potential to generate useful metadata to describe the contents of such archives on a large scale. Design/methodology/approach: The authors combined manual and automated/semi-automated methods of metadata development and curation. The authors developed a suitable domain-oriented taxonomy to classify a large text corpus of semi-structured interviews. To this end, the authors adapted the European Language Social Science Thesaurus (ELSST) to produce a concise, hierarchical structure of topics relevant in social sciences. The authors identified and tested the most promising natural language processing (NLP) tools supporting the Hungarian language. The results of manual and machine coding will be presented in a user interface. Findings: The study describes how an international social scientific taxonomy can be adapted to a specific local setting and tailored to be used by automated NLP tools. The authors show the potential and limitations of existing and new NLP methods for thematic assignment. The current possibilities of multi-label classification in social scientific metadata assignment are discussed, i.e. the problem of automated selection of relevant labels from a large pool. Originality/value: Interview materials have not yet been used for building manually annotated training datasets for automated indexing of scientifically relevant topics in a data repository. Comparing various automated-indexing methods, this study shows a possible implementation of a researcher tool supporting custom visualizations and the faceted search of interview collections. [ABSTRACT FROM AUTHOR]

As a representative of the fourth-generation light sources, the High Energy Photon Source (HEPS) in Beijing, China, utilizes a multi-bend achromat lattice to obtain an approximately 100 times emittance reduction compared with thirdgeneration light sources. New technologies bring new challenges to operate the storage ring. In order to meet the beam commissioning requirements of HEPS, a new framework for the development of high-level applications (HLAs) has been created. The key part of the new framework is a dual-layer physical module to facilitate the seamless fusion of physical simulation models with the real machine, allowing for fast switching between different simulation models to accommodate the various simulation scenarios. As a framework designed for development of physical applications, all variables are based on physical quantities. This allows physicists to analytically assess measurement parameters and optimize machine parameters in a more intuitive manner. To enhance both extensibility and adaptability, a modular design strategy is utilized, partitioning the entire framework into discrete modules in alignment with the requirements of HLA development. This strategy not only facilitates the independent development of each module but also minimizes inter-module coupling, thereby simplifying the maintenance and expansion of the entire framework. To simplify the development complexity, the design of the new framework is implemented using Python and is called Python-based Accelerator Physics Application Set (Pyapas). Taking advantage of Python's flexibility and robust library support, we are able to develop and iterate quickly, while also allowing for seamless integration with other scientific computing applications. HLAs for both the HEPS linac and booster have been successfully developed. During the beam commissioning process at the linac, Pyapas's ease of use and reliability have significantly reduced the time required for the beam commissioning operators. As a development framework for HLA designed for the new-generation light sources, Pyapas has the versatility to be employed with HEPS, as well as with other comparable light sources, due to its adaptability. [ABSTRACT FROM AUTHOR]

The book "Philosophy of Computer Science: An Introduction to the Issues and the Literature" by William J. Rapaport is a comprehensive monograph that analyzes the core issues defining the philosophy of computer science. It covers topics such as the nature of computer science, the definition of algorithms, the Church-Turing Thesis, the ontology of computer programs, and the relationship between computer science, ethics, and artificial intelligence. The book is intended for specialists in the field but can also be used as an introductory textbook for undergraduate teaching, requiring no background in philosophy or computer science. [Extracted from the article]

This paper introduces the Julia programming language as a dynamic, cost-effective, and efficient framework for implementing structural analysis packages. To achieve this, the finite element method was implemented for plane frames addressing the elastic instability problem through the Finite Element Method (FEM). Julia is a language open source, multiplatform, high-level and high-performance for technical and scientific computing, its compiler allows you to achieve speeds comparable to languages such as C and FORTRAN, but with more productive development dynamics due to its programming flexibility. Benchmarks between Julia and MATLAB are employed to discuss the processing costs, the programming techniques and paradigms used for computational performance. The results demonstrate that Julia performed the same analysis as the language used for comparison in 88.40% of the time, in addition to the fact that in loops comparisons case it reached 41.7% of the time for iteration, confirming its significant potential as a development tool of computational packages for structural analysis and scientific computing in general. [ABSTRACT FROM AUTHOR]

Deep neural networks are extensively used in text mining and Natural Language Processing is to enable computers to understand, analyze, and generate natural language data, such as text or speech, but semantic resources, such as taxonomies and ontologies, are not fully included in deep learning. In this paper, we use Deep Convolutional Neural Network (Deep CNN) to classify research papers using the Computer Science Ontology, an ontology of research areas in the field of computer science. It takes as input the abstract and keywords of a particular research paper and returns the relevant research topic. To evaluate our ontology, we used a gold standard dataset that includes research articles. To further improve text classification results, we propose to design a Deep CNN model. We then used ontology matching to reduce the classes and get better results. Experimental results show that the proposed approach outperforms the one with the highest precision, recall, and F1-score. [ABSTRACT FROM AUTHOR]

In-memory computing represents an effective method for modeling complex physical systems that are typically challenging for conventional computing architectures but has been hindered by issues such as reading noise and writing variability that restrict scalability, accuracy, and precision in high-performance computations. We propose and demonstrate a circuit architecture and programming protocol that converts the analog computing result to digital at the last step and enables low-precision analog devices to perform high-precision computing. We use a weighted sum of multiple devices to represent one number, in which subsequently programmed devices are used to compensate for preceding programming errors. With a memristor system-on-chip, we experimentally demonstrate high-precision solutions for multiple scientific computing tasks while maintaining a substantial power efficiency advantage over conventional digital approaches. [ABSTRACT FROM AUTHOR]

This paper analyzes the tradeoffs between performance and resilience against cyber attacks of applications organized as workflows. The static nature of current workflows is a major benefit to attackers. To combat this advantage, a promising new approach inspired to Moving Target Defense (MTDs) was developed to increase a workflow's robustness to cyber attacks. This approach is based on dynamic reconfigurations of workflow tasks to reduce an attacker's probability of succeeding in completing the reconnaissance phase before launching an attack. Dynamic reconfigurations increase the resilience of a workflow against cyber attacks but increase its execution time due to the overhead of reconfigurations. As a part of this paper, we developed metrics that capture the impact of reconfigurations on a workflow's execution time and resilience against cyber attacks. The paper also presents recursive algorithms for computing the execution time and the reconnaissance function of a workflow. Our analysis relied on extensive trace-driven simulations of workflows from five different traces from the Workflow Trace Archive (WTA) and we used 6000 workflows from three different domains: scientific computing, engineering, and industrial. Our analysis of the results showed that there is a significant difference at the 95% confidence level due to reconfiguration on the resilience of workflows and demonstrated a consistent behavior across all five trace domains. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new class of micro-electromechanical oscillators. Some investigations based on Melnikov's approach are applied for identifying some chaotic possibilities. We demonstrate also some specialized modules for investigating the dynamics of these oscillators. This will be included as an integral part of a planned much more general Web-based application for scientific computing. It turns out that the theoretical apparatus for studying the circuit implementation (design, fabricating, etc.) of the considered differential model for large parameter values is extremely complex and requires a serious investigation. This is the reason to offer this model to the attention of specialists working in this scientific direction. Some open problems related to the use of existing computer algebraic systems for the study of this class of oscillators for large values of n , m and N are also posed. In general, the entire article is subordinated to this frank conversation with the readers with the sole purpose being the professional upgrading of the specialized modules provided for this purpose in subsequent licensed versions of CAS. [ABSTRACT FROM AUTHOR]

The article discusses the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite mission, which is a joint effort between the European Space Agency (ESA) and the Japan Aerospace Exploration Agency (JAXA). The mission aims to improve weather forecasts and climate projections by studying cloud, aerosol, and radiation processes. EarthCARE will use novel instruments to retrieve more accurate properties of clouds, aerosols, and precipitation, and provide in-orbit verification of their impact on radiative fluxes. The article also pays tribute to Dr. Tobias Wehr, the mission scientist who passed away in 2023 and played a crucial role in the development of EarthCARE. [Extracted from the article]

We demonstrate an order of magnitude reduction in the sensitivity to optical crosstalk for neighboring trapped-ion qubits during simultaneous single-qubit gates driven with individual addressing beams. Gates are implemented via two-photon Raman transitions, where crosstalk is mitigated by offsetting the drive frequencies for each qubit to avoid first-order crosstalk effects from inter-beam two-photon resonance. The technique is simple to implement, and we find that phase-dependent crosstalk due to optical interference is reduced on the most impacted neighbor from a maximal fractional rotation error of 0.185 (4) without crosstalk mitigation to ≤ 0.006 with the mitigation strategy. Furthermore, we characterize first-order crosstalk in the two-qubit gate and avoid the resulting rotation errors for the arbitrary-axis Mølmer–Sørensen gate via a phase-agnostic composite gate. Finally, we demonstrate holistic system performance by constructing a composite CNOT gate using the improved single-qubit gates and phase-agnostic two-qubit gate. This work is done on the Quantum Scientific Computing Open User Testbed; however, our methods are widely applicable for individual addressing Raman gates and impose no significant overhead, enabling immediate improvement for quantum processors that incorporate this technique. [ABSTRACT FROM AUTHOR]

The graphics processing units (GPUs) are becoming a compelling acceleration strategy for geoscience numerical model due to their powerful computing performance. In this study, AMD's heterogeneous compute interface for portability (HIP) was implemented to port the GPU acceleration version of the Piecewise Parabolic Method (PPM) solver (GPU-HADVPPM) from the NVIDIA GPUs to China' s domestically GPU-like accelerators as GPU-HADVPPM4HIP, and further introduced the multi-level hybrid parallelism scheme to improve the total computational performance of the HIP version of CAMx (CAMx-HIP) model on the China' s domestically heterogeneous cluster. The experimental results show that the acceleration effect of GPU-HADVPPM on the different GPU accelerator is more obvious when the computing scale is larger and the maximum speedup of GPU-HADVPPM on the domestic GPU-like accelerator is 28.9 times. The hybrid parallelism with a message passing interface (MPI) and HIP enables achieve up to 17.2 times speedup when configure 32 CPU cores and GPU-like accelerators on the domestic heterogeneous cluster. And the OpenMP technology is introduced to further reduce the computation time of CAMx-HIP model by 1.9 times. More importantly, by comparing the simulation results of GPU-HADVPPM on NVIDIA GPUs and domestic GPU-like accelerators, it is found that the simulation results of GPU-HADVPPM on domestic GPU-like accelerators have less difference than the NVIDIA GPUs, and the reason for this difference may be related to the fact that the NVIDIA GPU sacrifices part of the accuracy for improved computing performance. All in all, the domestic GPU-like accelerators are more accuracy for scientific computing in the field of geoscience numerical models. Furthermore, we also exhibit that the data transfer efficiency between CPU and GPU has an important impact on heterogeneous computing, and point out that optimizing the data transfer efficiency between CPU and GPU is one of the important directions to improve the computing efficiency of geoscience numerical models in heterogeneous clusters in the future. [ABSTRACT FROM AUTHOR]

Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produce consistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using the IllinoisGRMHD code that is part of an open-source community software for simulation in relativistic astrophysics, the Einstein Toolkit. We verify that numerical results of simulating a single isolated neutron star with IllinoisGRMHD can be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find that IllinoisGRMHD reproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version of IllinoisGRMHD reproduces these results once we account for bug fixes which have occurred since the original publication. [ABSTRACT FROM AUTHOR]

The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a satellite mission implemented by the European Space Agency (ESA) in cooperation with the Japan Aerospace Exploration Agency (JAXA) to measure vertical profiles of aerosols, clouds, and precipitation properties together with radiative fluxes and derived heating rates. The data will be used in particular to evaluate the representation of clouds, aerosols, precipitation, and associated radiative fluxes in weather forecasting and climate models. The satellite embarks four instruments: the ATmospheric LIDar (ATLID), the Cloud Profiling Radar (CPR), the Multi-Spectral Imager (MSI), and the Broadband Radiometer (BBR). The science data acquired by the four satellite instruments are processed on ground. Calibrated instrument data – level 1 data products – and retrieved geophysical data products – level 2 data products – are produced in the ESA and JAXA ground segments. This paper provides an overview of the data processing chains of ESA and JAXA and explains the instrument level 1 data products and main aspects of the calibration algorithms. Furthermore, an overview of the level 2 data products, with references to the respective dedicated papers, is provided. [ABSTRACT FROM AUTHOR]

Randomized matrix algorithms have become workhorse tools in scientific computing and machine learning. To use these algorithms safely in applications, they should be coupled with posterior error estimates to assess the quality of the output. To meet this need, this paper proposes two diagnostics: a leave-one-out error estimator for randomized low-rank approximations and a jackknife resampling method to estimate the variance of the output of a randomized matrix computation. Both of these diagnostics are rapid to compute for randomized low-rank approximation algorithms such as the randomized SVD and randomized Nystr\"om approximation, and they provide useful information that can be used to assess the quality of the computed output and guide algorithmic parameter choices. [ABSTRACT FROM AUTHOR]

The main purpose of scientific research is to discover fundamental principles and solve practical problems. Although tremendous progress has been made on both fronts, the lack of effective tools and efficient organizational structure still stands as the main bottleneck for scientific progress. The rapid development of artificial intelligence (AI) offers a new possibility. In recent years, deep learning has had an impressive performance, both in helping to solve fundamental scientific problems and in improving the effectiveness of scientific research tools. A new set of infrastructure is emerging, leading us to a new paradigm, the "Android paradigm", for doing scientific research. [ABSTRACT FROM AUTHOR]

Matrix–matrix multiplication is of singular importance in linear algebra operations with a multitude of applications in scientific and engineering computing. Data structures for storing matrix elements are designed to minimize overhead information as well as to optimize the operation count. In this study, we utilize the notion of the compact diagonal storage method (CDM), which builds upon the previously developed diagonal storage—an orientation-independent uniform scheme to store the nonzero elements of a range of matrices. This study exploits both these storage schemes and presents efficient GPU-accelerated parallel implementations of matrix multiplication when the input matrices are banded and/or structured sparse. We exploit the data layouts in the diagonal storage schemes to expose a substantial amount of fine-grained parallelism and effectively utilize the GPU shared memory to improve the locality of data access for numerical calculations. Results from an extensive set of numerical experiments with the aforementioned types of matrices demonstrate orders-of-magnitude speedups compared with the sequential performance. [ABSTRACT FROM AUTHOR]

Simulating solute transport in heterogeneous porous media poses computational challenges due to the high-resolution meshing required for traditional solvers. To overcome these challenges, this study explores a mesh-free method based on deep learning to accelerate solute transport simulation. We employ Physics-informed Neural Networks (PiNN) with a periodic activation function to solve solute transport problems in both homogeneous and heterogeneous porous media governed by the advection-dispersion equation. Unlike traditional neural networks that rely on large training datasets, PiNNs use strong-form mathematical models to constrain the network in the training phase and simultaneously solve for multiple dependent or independent field variables, such as pressure and solute concentration fields. To demonstrate the effectiveness of using PiNNs with a periodic activation function to resolve solute transport in porous media, we construct PiNNs using two activation functions, sin and tanh, for seven case studies, including 1D and 2D scenarios. The accuracy of the PiNNs' predictions is then evaluated using absolute point error and mean square error metrics and compared to the ground truth solutions obtained analytically or numerically. Our results demonstrate that the PiNN with sin activation function, compared to tanh activation function, is up to two orders of magnitude more accurate and up to two times faster to train, especially in heterogeneous porous media. Moreover, PiNN's simultaneous predictions of pressure and concentration fields can reduce computational expenses in terms of inference time by three orders of magnitude compared to FEM simulations for two-dimensional cases. [ABSTRACT FROM AUTHOR]

This study uses a national longitudinal sample of 612 Latinx STEM bachelor's degree recipients across 171 higher education institutions to examine the STEM undergraduate experiences and institutional contexts that predict their values toward conducting research that will have a meaningful impact on underserved communities. Results show that several background and precollege characteristics, particular STEM majors, learning about science and technology in society, and institutional characteristics predict this empowerment outcome. While attending a Hispanic-Serving Institution (HSI) did not have a direct effect on the outcome, it was found to reduce the negative effect of majoring in math/statistics and computer science/tech. Implications for our current understanding of Latinx STEM success and the role of HSIs and PWIs are provided. [ABSTRACT FROM AUTHOR]

Graph energies and spectral radii have application in molecular computing and computer sciences. In this paper, we concentrate on new findings about the Symmetric Division Deg, (SDD) energies, and (SDD) spectral radii of p splitting and p shadow graphs built on the foundation of any regular graph. In fact it becomes natural to ask how the SDD energies and spectral radii of newly formed graphs depend on the SDD energies and spectral radii of the given regular graph. [ABSTRACT FROM AUTHOR]

Multiscale is a hallmark feature of complex systems, presenting challenges for traditional numerical methods due to their reliance on local Taylor series constraints. Further, multiscale techniques often face limitations in identifying appropriate heuristic closures used to handle unresolved scales or processes within the system being modeled. In this study, we develop an efficient time-stepping strategy for multiscale problems. The proposed method's novelty lies in synergizing the hierarchical deep learning formalism with an adaptive step-size estimation process to approximate dynamical system flow maps across timescales efficiently. The model is purely data-driven and provides improved forecasting accuracy compared to fixed-step neural network time steppers. To demonstrate the effectiveness of the proposed scheme, we provide numerical simulations for several canonical nonlinear systems and source codes for their implementation. We believe the method has the potential to benefit multiscale analysis of complex systems and encourage further investigation in this area. • Identifies potential drawbacks in the current multiscale methods. • Introduces efficient time-stepping algorithm for multiscale problems using hierarchical deep learning. • Demonstrates the application of adaptive time-stepping on canonical ODEs and PDEs with noisy measurements. [ABSTRACT FROM AUTHOR]

Multiscale is a hallmark feature of complex systems, presenting challenges for traditional numerical methods due to their reliance on local Taylor series constraints. Further, multiscale techniques often face limitations in identifying appropriate heuristic closures used to handle unresolved scales or processes within the system being modeled. In this study, we develop an efficient time-stepping strategy for multiscale problems. The proposed method's novelty lies in synergizing the hierarchical deep learning formalism with an adaptive step-size estimation process to approximate dynamical system flow maps across timescales efficiently. The model is purely data-driven and provides improved forecasting accuracy compared to fixed-step neural network time steppers. To demonstrate the effectiveness of the proposed scheme, we provide numerical simulations for several canonical nonlinear systems and source codes for their implementation. We believe the method has the potential to benefit multiscale analysis of complex systems and encourage further investigation in this area. • Identifies potential drawbacks in the current multiscale methods. • Introduces efficient time-stepping algorithm for multiscale problems using hierarchical deep learning. • Demonstrates the application of adaptive time-stepping on canonical ODEs and PDEs with noisy measurements. [ABSTRACT FROM AUTHOR]

Many problems in scientific computing and engineering fields lead to the solution of generalized absolute value equations (GAVE). In this paper, by simultaneously splitting both coefficient matrices in the differential and non-differential parts of the GAVE, a modified Newton-based matrix splitting (MNMS) iteration method is proposed. The MNMS iteration method not only covers the well-known generalized Newton iteration method as well as the recent proposed modified Newton-based iteration method and the Newton-based matrix splitting iteration method for solving the GAVE, but also results in a series of relaxation versions that are very flexible in real applications. Convergence properties of the MNMS iteration method are studied in detail when the coefficient matrices are positive definite matrices and H + -matrices. Finally, three numerical examples are presented to show the feasibility and effectiveness of the proposed MNMS iteration method. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors [1,2] , can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (TCN) as the neural network of choice to capture the history-dependent evolution of the non-local strain in a coarsely meshed domain. The quality of the network predictions governs the computational performance of I-FENN, and therefore we perform an extended investigation aimed at enhancing them. We explore a data-driven vs. physics-informed TCN setup to arrive at an optimum network training, evaluating the network based on a coherent set of relevant performance metrics. We address the crucial issue of training a physics-informed network with input data that span vastly different length scales by proposing a systematic way of input normalization and output un-normalization. We then integrate the trained TCN within the nonlinear iterative FEM solver and apply I-FENN to simulate the damage propagation analysis. I-FENN is always applied in mesh idealizations different from the one used for the TCN training, showcasing the framework's ability to be used at progressively refined mesh resolutions. We illustrate several cases that I-FENN completes the simulation using either a modified or a full Newton–Raphson scheme, and we showcase its computational savings compared to both the classical monolithic and staggered FEM solvers. We underline that we satisfy very strict convergence criteria for every increment across the entire simulation, providing clear evidence of the robustness and accuracy of I-FENN. All the code and data used in this work will be made publicly available upon publication of the article. • Non-local damage propagation is modeled for the first time with a TCN-based I-FENN. • I-FENN is faster than monolithic and staggered FEM and savings scale with model size. • I-FENN's robustness is shown by satisfying very strict convergence criteria. • Input/output normalization methods are applied to enhance the network training. • I-FENN is always applied in different training and testing mesh idealizations. [ABSTRACT FROM AUTHOR]

"Deep Learning and Scientific Computing with R torch" by Sigrid Keydana is a book that introduces an R interface for PyTorch, a popular deep learning framework. The book is well-written and easy to follow, with plenty of illustrations and examples. It is divided into three parts: the basics of torch, the implementation of deep neural networks, and the utility of torch beyond deep learning. The book covers topics such as tensors, automatic differentiation, neural network architecture, optimization algorithms, and various applications of torch. Overall, it is a valuable resource for R users interested in deep learning and scientific computing. [Extracted from the article]

The article introduces a new section structure for the SIAM Journal on Scientific Computing (SISC), including a focus on Scientific Machine Learning as a major development in the field of scientific computing. Topics include the development of accurate, efficient, and scalable machine learning algorithms for solving mathematical modeling problems, emphasizing the importance of reproducibility in the review process.

The numerical simulation of cardiac electrophysiology is a highly challenging problem in scientific computing. The Bidomain system is the most complete mathematical model of cardiac bioelectrical activity. It consists of an elliptic and a parabolic partial differential equation (PDE), of reaction–diffusion type, describing the spread of electrical excitation in the cardiac tissue. The two PDEs are coupled with a stiff system of ordinary differential equations (ODEs), representing ionic currents through the cardiac membrane. Developing efficient and scalable preconditioners for the linear systems arising from the discretization of such computationally challenging model is crucial in order to reduce the computational costs required by the numerical simulations of cardiac electrophysiology. In this work, focusing on the Bidomain system as a model problem, we have benchmarked two popular implementations of the Algebraic Multigrid (AMG) preconditioner embedded in the PETSc library and we have studied the performance on the calibration of specific parameters. We have conducted our analysis on modern HPC architectures, performing scalability tests on multi-core and multi-GPUs settings. The results have shown that, for our problem, although scalability is verified on CPUs, GPUs are the optimal choice, since they yield the best performance in terms of solution time. [ABSTRACT FROM AUTHOR]

High performance computing system, which takes an interconnection network as its infrastructure topology, has been utilized in scientific computing, big data analysis as well as artificial intelligence. With the rapid growth of infrastructure topology (interconnection network) in high performance computing system, the probability of network element malfunction increases dramatically. Generally, the reliability of interconnection network is measured by the traditional (edge) connectivity, which is limited by the minimum degree of the network. In order to overcome the defect, the matroidal connectivity has been newly proposed to extend the edge connectivity. For the regular network G , let B k (G) = { B 1 , B 2 , ... , B k } be a fixed edge partition ordered sequence of G and χ k = (x 1 , x 2 , ... , x k) be a sequence of k integers. For any D ⊆ E (G) , if | D ∩ B i | ≤ x i (i ∈ [ k ]) and G − D is disconnected, then D is named matroidal cut set of G. The matroidal connectivity of G , denoted by λ (G , B k (G)) , is the minimum size of matroidal cut set of G. In this work, we characterize the matroidal connectivity of a class of n -dimensional regular networks G n. To be more specific, we show that λ (G n , B n (G n)) = (k − l + 1) f (l + j) , where f (n) is a function about n , and l , k , n are positive integers (l ≥ 1), j = n − k. As empirical analysis, we directly determine the matroidal connectivity of some well-known interconnection networks, such as hypercube Q n , star graph S T n and alternating group graph A G n. • High performance computing system, which takes interconnection network as its infrastructure topology, has been utilized in scientific computing, big data analysis as well as artificial intelligence. For the regular networks G , we use λ (G , B k (G)) to denote matroidal connectivity of G (where B k (G) = { B 1 , B 2 , ... , B k } is a fixed edge partition ordered sequence of G), which is the minimum size of edge cut set D ⊆ ∪ i = 1 k B i so that G − D is disconnected. In this work, we characterize the matroidal connectivity of a class of n -dimensional regular networks G n. That is, λ (G n , B k (G n)) = (k − l + 1) f (l + j) , where l , k , j (l ≤ k ≤ n) are integers. As empirical analysis, we determine the matroidal connectivity of some kinds of well-known interconnection networks, such as hypercubes (Q n), star graph (S T n) and alternating group graph (A G n). [ABSTRACT FROM AUTHOR]

There is continuing debate about which climate-policy instruments are most appropriate to reduce emissions. Undertaking a global survey among scientists who published on climate policy, we provide a systematic overview of (dis)agreements about six main types of policy instruments. The survey includes various fields across the social and natural sciences. The results show that, on average, all instruments are considered important, with direct regulation receiving the highest rating and adoption subsidies and cap-and-trade the lowest. The latter is surprising given the theoretical advantages and real-world success of the EU-ETS. Next, clustering scientific fields based on how important they consider the instruments, we determine five distinct groups, with (a) ecological economists and (b) mathematics/computer science being most dissimilar from other discipline clusters. We explain disagreement through assessing the relative importance assigned to policy criteria effectiveness, efficiency, equity and socio-political feasibility, as well as researchers' attitudes and background. Paying special attention to carbon pricing, motivated by its contested key role, we identify three respondent clusters, namely 'enthusiasts', 'undecided', and 'skeptics'. Examining various policy arguments, we find that agreeing that carbon pricing effectively limits energy/carbon rebound and has potential to be harmonized globally have the strongest association with giving importance to this policy. • We survey researchers from diverse fields to examine views on climate policies. • Direct regulation is on average rated as most important. • Environmental and ecological economists hold contrasting views on cap-and-trade. • Support for carbon pricing relates to expectations of curbing rebound and global harmonization. • Many other factors are assessed, such as policy criteria, climate worry and ideology. [ABSTRACT FROM AUTHOR]

Crossflow filtration with pore blocking is an unsteady system affected by operating conditions. We model pore blocking and solve the governing partial differential equations using the method of lines. The loss in permeability is neither spatially nor temporally uniform. Operating at constant flux results in a greater permeate of clean water but at a higher operating cost than operating at constant pressure. • Mathematical model of unsteady crossflow filtration with pore blocking. • Numerical method of lines solver for differential–algebraic equations. • Operating at constant pressure is more efficient than at constant flux. [ABSTRACT FROM AUTHOR]

The article offers information on The Seventeenth Copper Mountain Conference on Iterative Methods (CMCIM), which took place virtually in April 2022.

• We present and analyze a NGAOR method for solving LCPs, in which one special case reduces to a NGSOR method. • We prove the convergence of the proposed methods when the system matrix is an - matrix (irreducible or strictly diagonally dominant matrix). • Experimental results highlight the effectiveness and efficiency of our proposed methods. The linear complementarity problems (LCPs) can be encountered in various scientific computing, management science, and operations research. In this study, we introduce and analyze a new generalized accelerated overrelaxation (NGAOR) method for solving LCPs, in which one special case reduces to a new generalized successive overrelaxation (NGSOR) method. Moreover, we prove the convergence of the proposed methods when the system matrix is an H -matrix (irreducible or strictly diagonally dominant matrix). Numerical results for several experiments are present to show the effectiveness and efficiency of the proposed methods. AMS classification: 65F10, 90C33 [ABSTRACT FROM AUTHOR]

Due to the complex behavior arising from non-uniqueness, symmetry, and bifurcations in the solution space, solving inverse problems of nonlinear differential equations (DEs) with multiple solutions is a challenging task. To address this, we propose homotopy physics-informed neural networks (HomPINNs), a novel framework that leverages homotopy continuation and neural networks (NNs) to solve inverse problems. The proposed framework begins with the use of NNs to simultaneously approximate unlabeled observations across diverse solutions while adhering to DE constraints. Through homotopy continuation, the proposed method solves the inverse problem by tracing the observations and identifying multiple solutions. The experiments involve testing the performance of the proposed method on one-dimensional DEs and applying it to solve a two-dimensional Gray-Scott simulation. Our findings demonstrate that the proposed method is scalable and adaptable, providing an effective solution for solving DEs with multiple solutions and unknown parameters. Moreover, it has significant potential for various applications in scientific computing, such as modeling complex systems and solving inverse problems in physics, chemistry, biology, etc. • Physics-informed neural networks are used to estimate unknown parameters with physical constraints. • HomPINNs helps to identify multiple solutions and solve inverse problems. • Numerical examples demonstrate its scalability and adaptability. • HomPINNs facilitates the discovery of previously unknown solutions. [ABSTRACT FROM AUTHOR]

Large simulations with many independent sub-simulations are common in scientific computing. There are numerous challenges, however, associated with performing such simulations in shared computing environments. For example, sub-simulations may have wildly varying completion times or not complete at all, leading to unpredictable runtimes as well as unbalanced and inefficient use of human and computational resources. In this study, we use the actor model of concurrent computation to improve both the resource utilization and fault tolerance for large-scale scientific computing simulations. More specifically, we use actors in the SUMMA model to manage a large-scale hydrological simulation over the North American continent with over 500,000 independent sub-simulations. We find that the actors implementation outperforms a standard array job submission as well as the job submission tool GNU Parallel by better balancing the computational load across processors. The actors implementation also improves fault tolerance and can eliminate the user intervention required to detect and re-submit failed jobs. [ABSTRACT FROM AUTHOR]

Interface problems have long been a major focus of scientific computing, leading to the development of various numerical methods. Traditional mesh-based methods often employ time-consuming body-fitted meshes with standard discretization schemes or unfitted meshes with tailored schemes to achieve controllable accuracy and convergence rate. Along another line, mesh-free methods bypass mesh generation but lack robustness in terms of convergence and accuracy due to the low regularity of solutions. In this study, we propose a novel method for solving interface problems within the framework of the random feature method (RFM). This approach utilizes random feature functions in conjunction with a partition of unity as approximation functions, and solves a linear least-squares system to obtain the approximate solution. In the context of interface problems, two innovative and crucial components are incorporated into the RFM. Firstly, we utilize two sets of random feature functions on each side of the interface, allowing for the inclusion of low regularity or even discontinuous behaviors in the solution. Secondly, the construction of the loss function is based on the assessment of the partial differential equation, initial/boundary conditions, and the interface condition on collocation points. This approach ensures that these conditions are equally satisfied. Consequently, the challenges arising from geometric complexity primarily manifest in the generation of collocation points, a task amenable to standard methods. Importantly, the proposed method retains its meshfree characteristics and robustness when addressing problems featuring intricate geometries. We validate our method through a series of linear interface problems with increasingly complex geometries, including two-dimensional elliptic and three-dimensional Stokes interface problems, a three-dimensional elasticity interface problem, a moving interface problem with topological change, a dynamic interface problem with large deformation, and a linear fluid–solid interaction problem with complex geometry. Our findings show that despite the solution often being only continuous or even discontinuous, our method not only eliminates the need for mesh generation but also maintains high accuracy, akin to the spectral collocation method for smooth solutions. Remarkably, for the same accuracy requirement, our method requires two to three orders of magnitude fewer degrees of freedom than traditional methods, demonstrating its significant potential for solving interface problems with complex geometries and predetermined intricate evolution. [ABSTRACT FROM AUTHOR]