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Sound occurs when vibrations travel through a medium. These vibrations, their propagation, and their interaction with the environment are dictated by laws of physics, and indirectly reflect underlying physical properties of things in the world. The sense of audition exists to measure sound and infer its causes in the world, so as to help organisms interact with the world around them. Audition is thus indirectly but intimately shaped by the physics of sound. In this primer, Josh McDermott, Vinayak Agarwal, and James Traer provide an overview of how physical principles shape sound in the world, and how implicit knowledge of these principles guides our sense of hearing. [ABSTRACT FROM AUTHOR]

The results of investigation of DNA-protein complexes by two independent structural methods, namely, molecular dynamics (MD) and small-angle X-ray scattering (SAXS), were compared. Molecular dynamics is a computational method enabling the visualization of the behavior of macromolecules in real media, which is based on the laws of physics, but it is limited by numerous simplifications. Small-angle X-ray scattering is an X-ray method, which allows the reconstruction of the three-dimensional structure of a system in solution from a one-dimensional small-angle scattering profile, but it faces the problem of ambiguity in solving inverse problems. The use of structural characteristics of the complexes determined by SAXS for the validation of 3D structural models generated by MD simulations made it possible to significantly reduce the ambivalence of theoretical predictions and demonstrated the efficiency of a combination of MD simulations and SAXS for solving problems of structural biology. [ABSTRACT FROM AUTHOR]

For understanding many real-world problems involving rarefied hypersonic, micro-, and nanoscale gas flows, the primary method may be the direct simulation Monte Carlo (DSMC). However, its computational cost is prohibitive in comparison with the Navier–Stokes–Fourier (NSF) solvers, eclipsing the advantages it provides, especially for situations where flow is in the near continuum regime or three-dimensional applications. This study presents an alternate computational method that bypasses this issue by taking advantage of data-driven modeling and nonlinear coupled constitutive relations. Instead of using numerical solutions of higher-order constitutive relations in conventional partial differential equation-based methods, we build compact constitutive relations in advance by applying deep neural network algorithms to available DSMC solution data and later combine them with the conventional finite volume method for the physical laws of conservation. The computational accuracy and cost of the methodology thus developed were tested on the shock wave inner structure problem, where high thermal non-equilibrium occurs due to rapid compression, for a range of Mach numbers from 2 to 10. The simulation results obtained with the computing time comparable to that of the NSF solver showed almost perfect agreement between the neural network-based combined finite volume method and DSMC and original DSMC solutions. We also present a topology of DSMC constitutive relations that allows us to study how the DSMC topology deviates from the NSF topology. Finally, several challenging issues that must be overcome to become a robust method for solving practical problems were discussed. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems. [ABSTRACT FROM AUTHOR]

Background/Objectives: Numerical simulation plays an important role in pharmaceutical preparation recently. Mechanistic models, as a type of numerical model, are widely used in the study of pharmaceutical preparations. Mechanistic models are based on a priori knowledge, i.e., laws of physics, chemistry, and biology. However, due to interdisciplinary reasons, pharmacy researchers have greater difficulties in using computer models. Methods: In this paper, we highlight the application scenarios and examples of mechanistic modelling in pharmacy research and provide a reference for drug researchers to get started. Results: By establishing a suitable model and inputting preparation parameters, researchers can analyze the drug preparation process. Therefore, mechanistic models are effective tools to optimize the preparation parameters and predict potential quality problems of the product. With product quality parameters as the ultimate goal, the experiment design is optimized by mechanistic models. This process emphasizes the concept of quality by design. Conclusions: The use of numerical simulation saves experimental cost and time, and speeds up the experimental process. In pharmacy experiments, part of the physical information and the change processes are difficult to obtain, such as the mechanical phenomena during tablet compression and the airflow details in the nasal cavity. Therefore, it is necessary to predict the information and guide the formulation with the help of mechanistic models. [ABSTRACT FROM AUTHOR]

This study presents the development of a so‐called Turbulent Kinetic Energy (TKE)‐l, or TKE‐l, parameterization of the diffusion coefficients for the representation of turbulent diffusion in neutral and stable conditions in large‐scale atmospheric models. The parameterization has been carefully designed to be completely tunable in the sense that all adjustable parameters have been clearly identified and the number of parameters has been minimized as much as possible to help the calibration and to thoroughly assess the parametric sensitivity. We choose a mixing length formulation that depends on both static stability and wind shear to cover the different regimes of stable boundary layers. We follow a heuristic approach for expressing the stability functions and turbulent Prandlt number in order to guarantee the versatility of the scheme and its applicability for planetary atmospheres composed of an ideal and perfect gas such as that of Earth and Mars. Particular attention has been paid to the numerical stability and convergence of the TKE equation at large time steps, an essential prerequisite for capturing stable boundary layers in General Circulation Models (GCMs). Tests, parametric sensitivity assessments and preliminary tuning are performed on single‐column idealized simulations of the weakly stable boundary layer. The robustness and versatility of the scheme are assessed through its implementation in the Laboratoire de Météorologie Dynamique Zoom GCM and the Mars Planetary Climate Model and by running simulations of the Antarctic and Martian nocturnal boundary layers. Plain Language Summary: In planetary atmospheres, turbulent motions actively contribute to the mixing of quantities such as heat, momentum, and chemical species. Such motions are not resolved in coarse‐grid atmospheric models and have to be parameterized. The parameterization of turbulent mixing should ideally be based on physical laws and sufficiently sophisticated to realistically represent the full spectrum of motions over the full range of stability encountered in the atmospheres. However, it also necessarily contains a number of closure parameters not always well identified and whose values are determined empirically—thereby questioning the universality of the parameterization and its potential application over the full globe or even to other planets—or adjusted to guarantee the numerical stability of the model. This study presents the design of a turbulent mixing parameterization that can be fully calibrated and applied in planetary atmospheres such as that of Mars. We then calibrate the parameterization on an idealized simulation set‐up and test its robustness and performance by running simulations of the Antarctic and Martian atmospheres. Key Points: A simple TKE‐l turbulent diffusion scheme is developed in a semi‐heuristic way for applications in models of the Earth and Mars atmospheresAll adjustable parameters are clearly identified and the number of parameters is minimized to thoroughly assess the parametric sensitivityOnce tuned on GEWEX Atmospheric Boundary Layer Study 1 1D simulations, the scheme is able to capture the Antarctic and Martian stable boundary layers in 3D simulations [ABSTRACT FROM AUTHOR]

Moral realists often disagree about the nature of moral properties. These properties can be natural (as per naturalistic moral realism) or non-natural. But it is unclear how we should understand the notion of naturalness employed in these discussions. In this paper I propose a novel account of moral naturalness. I suggest that a property F is natural iff F falls within the scope of a natural law. In turn, a law is natural when it figures in a nomic nexus involving the laws of physics. [ABSTRACT FROM AUTHOR]

The Regular Phantom Black Hole (RPBH)s are of theoretical and observational importance, some of their properties have been studied. In this work, some of thermodynamical properties such as entropy, temperature, etc., in three background cases, that is, flat, de–Sitter (dS) and Anti–de Sitter (AdS) are studied. Many of the RPBH properties, including horizon radius, are (directly or indirectly) dependent on a scale parameter b$b$. Due to the slightly different structure from Schwarzschild—like metrics, the method to express relations between thermodynamical variables requires a new function of the scale parameter. The local and global stability through the Heat Capacity (HC) and Gibbs free Energy (GE), respectively are also treated. In the AdS case, the regularized metric allows a Hawking‐Page like phase transition of first order. The calculations and graphs show the results in the flat background, are very similar to Schwarzschild black hole and the asymptotically AdS RPBH is more compatible with physical laws than the dS and flat backgrounds. [ABSTRACT FROM AUTHOR]

With the advancement of AI‐generated human motion, it is of increasing importance to think about how we distinguish real human motion from machine‐generated movements. In this study, we recruited professional performers to use the whole body to make a short movement to inform potential observers that they are real humans (instead of machines). Their movements were captured with a motion capture system (Vicon) and later reduced to dynamic point‐like displays (biological motion). They were interviewed after the recording to provide their acting strategies. Naive observers who did not participate in the motion data collection were recruited to watch these videos and judge whether the biological motions looked human‐like or not (YES/NO), as well as to report their judging criteria. The major factors extracted from these reports include kinematics, context, body mechanics, and principles of physical laws. We discuss the impact of these criteria and how they may possibly help improve the future generation of human‐like motions. [ABSTRACT FROM AUTHOR]

Evolution requires selection. Molecular/chemical/preDarwinian evolution is no exception. One molecule must be selected over another for molecular evolution to occur and advance. Evolution, however, has no goal. The laws of physics have no utilitarian desire, intent or proficiency. Laws and constraints are blind to "usefulness." How then were potential multi-step processes anticipated, valued and pursued by inanimate nature? Can orchestration of formal systems be physico-chemically spontaneous? The purely physico-dynamic self-ordering of Chaos Theory and irreversible non-equilibrium thermodynamic "engines of disequilibria conversion" achieve neither orchestration nor formal organization. Natural selection is a passive and after-the-fact-of-life selection. Darwinian selection reduces to the differential survival and reproduction of the fittest already-living organisms. In the case of abiogenesis, selection had to be 1) Active, 2) Pre-Function, and 3) Efficacious. Selection had to take place at the molecular level prior to the existence of non-trivial functional processes. It could not have been passive or secondary. What naturalistic mechanisms might have been at play? [ABSTRACT FROM AUTHOR]

With the global expansion of geothermal energy, accurate prediction of pressure and temperature during drilling has become essential for ensuring the safety and efficiency of geothermal wells. Traditional numerical methods, however, often struggle to handle complex wellbore environments due to their high data demands and limited computational accuracy. To address these challenges, this paper introduces an innovative predictive model based on Physics-Informed Neural Networks (PINNs). By integrating physical laws with deep learning, the model theoretically surpasses the limitations of conventional methods. Trained on pressure and temperature data from a geothermal well in the Xiong'an area, the model demonstrates exceptional accuracy and robustness. Additionally, the model was rigorously tested under extreme wellbore conditions, showcasing its strong generalization capabilities. The findings suggest that PINNs offer a highly advantageous solution for geothermal drilling, with significant potential for practical engineering applications. [ABSTRACT FROM AUTHOR]

Life on Earth uses DNA as the central template for self‐replication, genetic encoding, and information transfer. However, there are no physical laws precluding life's existence elsewhere in space, and alternative life forms may not need DNA. In the search for exobiology, knowing what to look for as a biosignature remains a challenge – especially if it is not from the obvious list of biologic building blocks. Clues from chemicals recently discovered on Mars and in the Taurus Molecular Cloud 1 (TMC‐1), show that intriguing organic compounds exist beyond Earth, which could provide a starting point for unconventional exobiotic designs. Here we present a new potential self‐replicating system with structural similarities to recently discovered compounds on Mars and TMC‐1. Rather than using DNA's hydrogen‐bonding motif for reliable base‐paring, our design employs sulfur‐nitrogen interactions to selectively template unique benzothiadiazole units in sequence. We synthesized and studied two versions of this system, one reversible and the other irreversible, and found experimental evidence of self‐replication in d‐chloroform solvent. These results are part of a larger pursuit in our lab for developing a basis for a potential exobiological system using starting blocks closely related to these cosmic compounds. [ABSTRACT FROM AUTHOR]

Dispersion coefficients and the average solute transport velocity are pivotal for groundwater solute transport modeling. Accurately and efficiently determining these parameters is challenging due to difficulties in directly correlating them with pore‐space structure. To address this issue, we introduced the Physics‐enhanced Convolutional Neural Network‐Transformer (PhysenCT‐Net), an innovative model designed to concurrently estimate the longitudinal dispersion coefficient and average solute transport velocity in three‐dimensional porous media. PhysenCT‐Net exhibited excellent predictive performance on unseen testing datasets and significantly reduced computational demands. Comprehensive evaluations confirmed its robust generalization across various flow conditions and pore structures. Notably, the longitudinal dispersion coefficient predictions closely align with established empirical relationships involving Péclet number, affirming the model's physical interpretability and potential to aid in simulating transport phenomena in porous media. Plain Language Summary: The migration of solutes in geological media is a complex process that involves both dispersion and advective motions. Therefore, the dispersion coefficient and advective flow rate are crucial parameters for modeling and predicting solute migration, with the former being determined by the complex flow field and solute diffusion coefficient within the medium. These two parameters are typically obtained through the experiment results of solute concentration distribution or through complex numerical simulations, which take a lot of time and resources. In this letter, we introduce a deep learning model that can provide a more efficient way to determine the two parameters describing substance movements. The model uses three‐dimensional images and properties of the media, the diffusion coefficient of solute and the mean flow speed of water as the inputs. We tested the model under a variety of conditions, and the dispersion coefficients predicted by the trained model agreed well with physical laws. The model can not only improve how we estimate these parameters, but also help us understand the physical laws that govern how substances move through groundwater. This could lead to better ways of modeling and managing these important water resources. Key Points: Accurately predicting key parameters of the advection‐dispersion equation for solute transport in porous mediaDeveloping direct mapping relationships between three‐dimensional porous media images and parameters by the physics‐enhanced neural networkNetwork demonstrates exceptional prediction accuracy and generalization, providing parameters that align with empirical formulas [ABSTRACT FROM AUTHOR]

This study focuses on the interannual variability of summertime intraseasonal precipitation intensity (SIPI) over South China (SC) in the 30–90‐day range. The results demonstrate a significant increase in the interannual variability of SIPI over SC around the mid‐1990s. By comparing atmospheric and oceanic anomalies between two periods, P1 (1984–1993) and P2 (1994–2003), we identify that both the Silk Road pattern (SRP) and the western Pacific subtropical high (WPSH) play a role in influencing this interdecadal increase in SIPI interannual variability. These system anomalies are modulated by sea surface temperature anomalies (SSTA) in the North Atlantic (NA) and equatorial East Pacific (EP). During years with positive precipitation anomalies in P2, warming EP SSTA induces anomalous Walker cell and local Hadley cell, weakening the WPSH and enhancing convective activity, and increasing moisture transport over SC. The warming NA SSTA near 45°N triggers a more southerly propagation of SRP, thereby providing additional disturbance energy that enhances summer precipitation over SC. Conversely, during years with negative precipitation anomalies in P2, these processes occur in reverse. The enhanced interannual variability of local convective activity from P1 to P2 leads to a corresponding increase in the interannual variability of the WPSH, which, coupled with an amplified interannual variability of SRP, results in heightened interannual variability of SIPI. Additionally, the composite analysis of 30–90‐day ISO events further substantiates the underlying mechanisms driving the interdecadal amplification of SIPI interannual variability. Diagnostic results reveal that the 30–90‐day horizontal wind induces heightened convergence of background moisture over SC. Plain Language Summary: Precipitation in South China is prevalent and primarily occurs from April to September, leading to significant enormous economic losses and casualties due to flood disasters almost every year during this period. Influenced by the East Asian summer monsoon, summer precipitation in South China exhibits diverse variabilities at multiple scales, including interdecadal, interannual, intraseasonal and synoptic variability. Moreover, the intraseasonal variability of precipitation in South China holds great importance for extended‐range forecasting and has been extensively investigated. With in‐depth research, it has been revealed that there are also significant physical laws governing the long‐term variations in intraseasonal precipitation. This study focuses on investigating the interdecadal changes in the interannual variability in summertime intraseasonal precipitation intensity over South China during the past four decades and exploring the potential underlying mechanisms. The findings can enhance our comprehension of the mechanisms driving long‐term variations in intraseasonal precipitation pattern in South China. Key Points: The interannual variability of 30–90‐day summer intraseasonal precipitation over South China exhibited an increase in the mid‐1990sThe Silk Road pattern (SRP) and the western Pacific subtropical high (WPSH) primarily contribute to this increaseThe occurrence of SRP and WPSH anomalies is modulated by SST warming in the North Atlantic and the equatorial East Pacific, respectively [ABSTRACT FROM AUTHOR]

A long‐held tenet in computer science asserts that the training of deep learning is analogous to an alchemical furnace, and its "black box" signature brings forth inexplicability. For electromagnetic metasurfaces, the related intelligent applications also get stuck into such a dilemma. Although the past 5 years have witnessed a proliferation of deep learning‐based works across complex photonic scenarios, they neglect the already existing but untapped physical laws. Here, the intrinsic correlation between the real and imaginary parts of the spectra are revealed using Kramers–Kronig relations, which is then mimicked by bidirectional information flow in neural network space. Such consideration harnesses the missing spectral connection to extract crucial features effectively. The bidirectional recurrent neural network is benchmarked in metasurface inverse design and compare it with a fully‐connected neural network, unidirectional recurrent neural network, and attention‐based transformer. Beyond the improved accuracy, the study examines the intermediate information products and physically explains why different network structures yield different performances. The work offers explicable perspectives to utilize physical information in the deep learning field and facilitates many data‐intensive research endeavors. [ABSTRACT FROM AUTHOR]

Cavity optomechanical (COM) sensors, featuring efficient light–motion couplings, have been widely used for ultrasensitive measurements of various physical quantities ranging from displacements to accelerations or weak forces. Previous works, however, have mainly focused on reciprocal COM systems. Here, we propose how to further improve the performance of quantum COM sensors by breaking reciprocal symmetry in purely quantum regime. Specifically, we consider a spinning COM resonator and show that by selectively driving it in opposite directions, highly nonreciprocal optical squeezing can emerge, which in turn provides an efficient way to surpass the standard quantum limit which is otherwise unattainable for the corresponding reciprocal devices. Our work confirms that breaking reciprocal symmetry, already achieved in diverse systems well beyond spinning systems, can serve as a new strategy to further enhance the abilities of advanced quantum sensors, for applications ranging from testing fundamental physical laws to practical quantum metrology. [ABSTRACT FROM AUTHOR]

Physics-informed neural networks (PINNs) represent a significant advancement at the intersection of machine learning and physical sciences, offering a powerful framework for solving complex problems governed by physical laws. This survey provides a comprehensive review of the current state of research on PINNs, highlighting their unique methodologies, applications, challenges, and future directions. We begin by introducing the fundamental concepts underlying neural networks and the motivation for integrating physics-based constraints. We then explore various PINN architectures and techniques for incorporating physical laws into neural network training, including approaches to solving partial differential equations (PDEs) and ordinary differential equations (ODEs). Additionally, we discuss the primary challenges faced in developing and applying PINNs, such as computational complexity, data scarcity, and the integration of complex physical laws. Finally, we identify promising future research directions. Overall, this survey seeks to provide a foundational understanding of PINNs within this rapidly evolving field. [ABSTRACT FROM AUTHOR]

Hydraulic models are essential for predicting, estimating, analyzing, and optimizing long-distance water supply systems. Accurate calibration of these models is crucial for effectively representing the behavior of such complex systems and achieving a comprehensive understanding. However, the limited availability of measured data in actual systems often leads to an ill-posed problem characterized by more variables than equations. This paper proposes a calibration framework based on artificial neural networks (ANN) and adaptive particle swarm optimization to address this challenge. The influence of the number of measurement points and the measurement errors on the calibration results is analyzed within an actual long-distance water supply system featuring multi-branch pipelines. Results showed that ANNs can accurately reconstruct flow rates and pressures at unmeasured nodes, even with very limited measurement data. Compared to conventional calibration methods, the proposed framework can reduce the influence of measurement data uncertainty on calibration results, achieving better calibration accuracy. Additionally, by introducing regularization into the loss function, the constraints of physical laws are incorporated into the neural network's training process, further enhancing the calibration accuracy of the model. [ABSTRACT FROM AUTHOR]

Equation discovery aims to directly extract physical laws from data and has emerged as a pivotal research domain in nonlinear systems. Previous methods based on symbolic mathematics have achieved substantial advancements, but often require handcrafted representation rules and complex optimization algorithms. In this paper, we introduce a novel framework that utilizes natural language-based prompts to guide large language models (LLMs) in automatically extracting governing equations from data. Specifically, we first utilize the generation capability of LLMs to generate diverse candidate equations in string form and then evaluate the generated equations based on observations. The best equations are preserved and further refined iteratively using the reasoning capacity of LLMs. We propose two alternately iterated strategies to collaboratively optimize the generated equations. The first strategy uses LLMs as a black-box optimizer to achieve equation self-improvement based on historical samples and their performance. The second strategy instructs LLMs to perform evolutionary operations for a global search. Experiments are conducted on various nonlinear systems described by partial differential equations, including the Burgers equation, the Chafee–Infante equation, and the Navier–Stokes equation. The results demonstrate that our framework can discover correct equations that reveal the underlying physical laws. Further comparisons with state-of-the-art models on extensive ordinary differential equations showcase that the equations discovered by our framework possess physical meaning and better generalization capability on unseen data. [ABSTRACT FROM AUTHOR]

To better realize the design and application of particle damping technology, this study explores the physical laws of vibration in granular assemblies within a confined space under forced vibration conditions and investigates the frequency-dependent characteristics of vibration state transitions in cylindrical granular bodies using experimental methods and discrete element methods. The findings indicate that under forced vibration conditions, granular assemblies exhibit five typical states, namely, solid-like, micro-vibrational, intermediate vibrational, strong vibrational, and gas-like. The vibration patterns demonstrate significant frequency-dependent characteristics, and the loss capacity of different vibration states of granular assemblies is discussed. The transition boundary of different vibration states of granular assemblies is analyzed. The influence of particle size, mass, and granular material on the parameters of the vibration state transition boundary is analyzed. The conversion and control of the vibration state of granular assemblies will be beneficial to achieve engineering applications by changing the dynamic behavior of the particles. [ABSTRACT FROM AUTHOR]

This article explores the metaphysics of nonreductive physicalism and its connection to F.H. Bradley's critique of relations. Nonreductive physicalism argues that while the physical domain is fundamental, there are higher-level items, like mental properties, that are distinct from the physical but dependent on it. Bradley's critique challenges the idea of relations as external entities, suggesting that they cannot be explained without an infinite regress of relating relations. The article discusses how this critique challenges the concept of supervenience, a key component of nonreductive physicalism, and suggests the need for a more intimate inter-level relation between the physical and the mental. The text also discusses the significance of relations in metaphysics, particularly in the context of nonreductive physicalism. [Extracted from the article]

This paper develops a challenge to standard libertarian views that is based on an imagined neuroscientificdiscovery that is incompatible with satisfaction of a standard libertarian requirement for mainstream free decision making, and it explores potential libertarian responses to this discovery. The requirement at issue may beformulated as follows: In mainstream cases, an agent freely decided at t to A only if, given the past and the laws of nature, the agent was able right up to t to do something else intentionally at t than decide to A. The imagined discovery is about a point of no return for the making of any particular decision in a mainstream scenario. [ABSTRACT FROM AUTHOR]

Solving problems in fluid mechanics plays an essential role in science and engineering, especially when it comes to optimal design, reconstruction of biomedical and geophysical flows, parameter estimation, and more. In some of these problems, only part of the data (or parameters) are known, and they fall within the broad categories of inverse and mixed problems. Thus, solving them using traditional methods is challenging or sometimes even impossible. Moreover, generating simulated data for such problems can become very costly since simulations need to be performed several times to either discover missing physics or calibrate the free parameters in the model. One possible alternative for overcoming these drawbacks is through the use of Physics-Informed Neural Networks—PINNs, in which we approximate the problem's solution using neural networks (NNs) while incorporating the known data and physical laws when training it and also easily enabling us to take advantage of computational resources like GPUs. Here, we show a Level-Set PINN-based framework for reconstructing the velocity field for bubble flows. Given only the bubble position, we apply the framework to reconstruct gas bubbles rising in viscous liquid problems. We use synthetic data generated by adaptive mesh refinement and coarsening simulations with a different method, a phase-field approach. The only data provided is a set of snapshots containing the bubble position, i.e., the phase field, from which we try to infer the velocities. Our approach does not require any reinitialization scheme, as is usual when using a level-set approach and traditional numerical methods. Such a scheme can reconstruct the flow quantities with reasonable accuracy, and it is straightforward to parallelize when using a data-parallel approach. [ABSTRACT FROM AUTHOR]

The regularized lattice Boltzmann method (RLBM) has the advantage of improving the accuracy of numerical simulations without increasing the computational overhead. The RLBM usually uses the standard Cartesian grid in numerical simulation. This paper introduces the multi-layer grid technique, the multi-layer grid has different resolutions, which is also more consistent with the physical laws of the flow field. Based on this, a multi-layer grid regularized lattice Boltzmann method (ML-RLBM) for solving complex flow problems is proposed. The proposed method performs local refinement of the grid on the standard Cartesian grid to achieve a multi-layer grid, which can reduce the computational overhead. In order to realize the local grid refinement easily, this paper adopts the center point format to store the multi-layer grid. The ML-RLBM designs a buffer scheme between different layers of grids, which realizes that only spatial interpolation is required at the interface of different layers of grids without temporal interpolation. The numerical results show that the ML-RLBM has excellent numerical accuracy and numerical stability, and it can effectively reduce the computational cost. [ABSTRACT FROM AUTHOR]

Current artifact aims to investigate attributes of nanofluidic transport mechanism developed for dynamics of Williamson liquid over an extendable cylinder with novel physical aspects of magnetic field. Additionally, Newtonian heating is accounted at cylinder surface and Reynold viscosity model is implemented to delineate the aspects of temperature dependent viscosity. Thermophoresis, Brownian motion, and variable viscosity effects have been studied for heat and mass transfer analysis. Formulation of problems characterized by governing physical laws is conceded in the form of dimensional partial differential setup by executing boundary layer approach. Conversion of attained differential system into ODE's is engrossed by capitalizing similarity transformations. Numerical procedures (Runge-Kutta and shooting) are implemented to resolve the problem and to attain solution. Effectiveness of sundry variables on associated distributions is revealed through graphical and tabular manner. Quantities of interest are also manipulated against the involved variables. Comparison of results to certify the numerical code is checked by making assessment with existing studies and found remarkable agreement. [ABSTRACT FROM AUTHOR]

We follow the assumption that relativistic causality is a key element in the structure of quantum mechanics and integrate the speed of light, c, into quantum mechanics through the postulate that the (reduced) Planck constant is a function of c with a leading order of the form ℏ c ∼ Λ / c p for a constant Λ > 0 , and p > 1. We show how the limit c → ∞ implies classicality in quantum mechanics and explain why p has to be larger than 1 . As the limit c → ∞ breaks down both relativity theory and quantum mechanics, as followed by the proposed model, it can then be understood through similar conceptual physical laws. We further show how the position-dependent speed of light gives rise to an effective curved space in quantum systems and show that a stronger gravitational field implies higher quantum uncertainties, followed by the varied c. We then discuss possible ways to find experimental evidence of the proposed model using set-ups to test the varying speed of light models and examine analogies of the model based on electrons in semiconductor heterostructures. [ABSTRACT FROM AUTHOR]

In this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range 1 × 10 − 6 – 1 × 10 − 4 , while the gamma analysis results in a 90–100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters. [ABSTRACT FROM AUTHOR]

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Concepts and laws of physics have been a valuable source of inspiration for engineers to overcome human challenges and problems. Classification is an important example of such problems that play a major role in various fields of engineering sciences. It is shown that discriminative classifiers tend to outperform their generative counterparts, especially in the presence of sufficient labeled training data. In this paper, we present a new physics-inspired discriminative classification method using minimum potential lines. To do this, we first consider two groups of fixed point charges (as two classes of data) and a movable classifier line between them. Then, we find a stable position for the classifier line by minimizing the total potential integral on the classifier line due to the two groups of point charges. Surprisingly, it will be shown that the obtained classifier is actually an uncertainty-based classifier that minimizes the total uncertainty of the classifier line. Experimental results show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The ability of living organisms to persist, grow, evolve and invade environments seemingly challenges physical laws. Emerging Autonomous Systems representing autocatalytic cycles constituted of energized components in a state of Dynamic Kinetic Stability feature some of these properties. These simple theoretical models can grow, can be transferred but need an initiation to emerge and can collapse. Moreover, they can undergo kinetic selection in a way consistent with Darwinian behaviour, though they lack the ability to undergo change. The mere existence of these systems and their open‐ended growth potential are proposed to constitute a transmissible factor of a non‐coded kind. The onset and selection of epigenetic factors may therefore have preceded that of genetic polymers. Here is addressed the question of how these systems may arise from the diversity exhibited by abiotic organic matter, sometimes associated with intractable mixtures, which may actually be useful in providing initiators. The Darwinian description of evolution may therefore be merged without critical discontinuity within an origin scenario. Accordingly, such a theory would rests solely on physicochemical laws beginning with the potential of emerging autonomous systems to compete and invade the space dimension, and to further develop along other available dimensions including variability and, possibly, cognition. [ABSTRACT FROM AUTHOR]

We present a machine learning framework capable of consistently inferring mathematical expressions of hyperelastic energy functionals for incompressible materials from sparse experimental data and physical laws. To achieve this goal, we propose a polyconvex neural additive model (PNAM) that enables us to express the hyperelastic model in a learnable feature space while enforcing polyconvexity. An upshot of this feature space obtained via the PNAM is that (1) it is spanned by a set of univariate basis functions that can be re‐parametrized with a more complex mathematical form, and (2) the resultant elasticity model is guaranteed to fulfill the polyconvexity, which ensures that the acoustic tensor remains elliptic for any deformation. To further improve the interpretability, we use genetic programming to convert each univariate basis into a compact mathematical expression. The resultant multi‐variable mathematical models obtained from this proposed framework are not only more interpretable but are also proven to fulfill physical laws. By controlling the compactness of the learned symbolic form, the machine learning‐generated mathematical model also requires fewer arithmetic operations than its deep neural network counterparts during deployment. This latter attribute is crucial for scaling large‐scale simulations where the constitutive responses of every integration point must be updated within each incremental time step. We compare our proposed model discovery framework against other state‐of‐the‐art alternatives to assess the robustness and efficiency of the training algorithms and examine the trade‐off between interpretability, accuracy, and precision of the learned symbolic hyperelastic models obtained from different approaches. Our numerical results suggest that our approach extrapolates well outside the training data regime due to the precise incorporation of physics‐based knowledge. [ABSTRACT FROM AUTHOR]

The concept of symmetry under various transformations of quantities describing basic natural phenomena is one of the fundamental principles in the mathematical formulation of physical laws. Starting with Noether's theorems, we highlight some well–known examples of global symmetries and symmetry breaking on the particle level, such as the separation of strong and electroweak interactions and the Higgs mechanism, which gives mass to leptons and quarks. The relation between symmetry energy and charge symmetry breaking at both the nuclear level (under the interchange of protons and neutrons) and the particle level (under the interchange of u and d quarks) forms the main subject of this work. We trace the concept of symmetry energy from its introduction in the simple semi-empirical mass formula and liquid drop models to the most sophisticated non-relativistic, relativistic, and ab initio models. Methods used to extract symmetry energy attributes, utilizing the most significant combined terrestrial and astrophysical data and theoretical predictions, are reviewed. This includes properties of finite nuclei, heavy-ion collisions, neutron stars, gravitational waves, and parity–violating electron scattering experiments such as CREX and PREX, for which selected examples are provided. Finally, future approaches to investigation of the symmetry energy and its properties are discussed. [ABSTRACT FROM AUTHOR]

Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation, including computational fluid dynamics (CFD), is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning method by integrating the conventional neural networks with the governing physical laws to predict the melt pool dynamics, such as temperature, velocity, and pressure, without using any training data on velocity and pressure. This approach avoids solving the nonlinear Navier-Stokes equation numerically, which significantly reduces the computational cost (if including the cost of velocity data generation). The difficult-to-determine parameters' values of the governing equations can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the extra penalty by incorporating governing PDEs, initial conditions, and boundary conditions in the PINN model. [ABSTRACT FROM AUTHOR]

Advancements in Artificial Intelligence (AI), particularly in Machine Learning (ML), are increasingly being utilized to address complex challenges in spacecraft technologies. One of the fundamental areas of focus is the thermal regulation system, which is crucial for the optimal functioning of the spacecraft. This paper introduces an innovative approach that employs Physics-Informed Neural Networks (PINNs) to model and predict temperature distributions within spacecraft. Unlike traditional neural networks, PINNs incorporate known physical laws, in this case, the heat equation, to ensure that the model's predictions are both accurate and physically consistent. We present a detailed methodology for developing a PINN that can accurately predict temperature variations over time, using sparse and noisy temperature data from a spacecraft. The proposed model is trained using a combination of data-driven and physics-informed loss functions, ensuring that the predictions adhere to the underlying physics of heat transfer. The results demonstrate the efficacy of PINNs in improving the accuracy of thermal predictions, which can significantly enhance the performance of the spacecraft's thermal regulation system. This work paves the way for further integration of physics-informed ML models in various aspects of spacecraft design and operation, offering a robust framework for tackling complex physical phenomena in space exploration. [ABSTRACT FROM AUTHOR]

With the widespread use of machine learning (ML) technology, the operational efficiency and responsiveness of power grids have been significantly enhanced, allowing smart grids to achieve high levels of automation and intelligence. However, tree ensemble models commonly used in smart grids are vulnerable to adversarial attacks, making it urgent to enhance their robustness. To address this, we propose a robustness enhancement method that incorporates physical constraints into the node-splitting decisions of tree ensembles. Our algorithm improves robustness by developing a dataset of adversarial examples that comply with physical laws, ensuring training data accurately reflects possible attack scenarios while adhering to physical rules. In our experiments, the proposed method increased robustness against adversarial attacks by 100% when applied to real grid data under physical constraints. These results highlight the advantages of our method in maintaining efficient and secure operation of smart grids under adversarial conditions. [ABSTRACT FROM AUTHOR]

Despite its status as a cult novel, adaptations of Mark Z. Danielewski's House of Leaves (2000) have been rare. This is not surprising, considering that the book's unusual form is intrinsically connected to its equally unusual content. The novel's story concerns a film about a house whose dimensions defy the laws of physics. The novel continually highlights the impossibility of its own filmic adaptation. Many of the main characters underline that the film medium inherently falls short of representing the unnatural space that is at the heart of the story. One successful adaptation is Das Haus (2010), a German radio play version of the novel that presents the story of House of Leaves in three different productions that were broadcast on three stations simultaneously. Listeners were invited to switch between the channels to find their own way through the story. This article focuses on the topic of space as it relates to sound in both the novel and the radio play adaptation, demonstrating how Das Haus relies on medium-specific elements to represent the unnatural narrative space of the story. [ABSTRACT FROM AUTHOR]

People have long had a problem: the equations of motion that reflect the laws of physics are invariant under time inversion, while there always are irreversible processes for gases composed of microscopic particles. This article solves the problem. The point is that we should distinguish between the concepts of the equation of motion and concrete motion. We also need to distinguish between the concepts of time-inverse motion and reverse motion. The former is anticlockwise, which is a fictional motion, while the latter is clockwise. For the single-particle motions in classical mechanics and in quantum mechanics, we present mathematical expressions for time-inversion motion and reverse motion, respectively. We demonstrate that single-particle motion is irreversible. The definition of the reversibility of two-particle collisions is given. According to the definition, the two-particle collision as a microscopic motion process is irreversible. Consequently, for a gas consisting of a large number of particles colliding with each other, its movement should be irreversible, unless the condition of detailed balance is met. We provide a physical explanation for detailed balance, which does not concern the meaning of microscopic reversibility. The detailed balance means that after a pair of reciprocal collisions occur, the distribution function of the particles remains unchanged. Therefore, microscopic two-particle collision events are irreversible, but the statistical average of a large number of collision events makes it possible for the macroscopic process of a gas to be reversible. Conclusively, we clarify the microscopic mechanism of the irreversible process of gases. [ABSTRACT FROM AUTHOR]