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Metaheuristic algorithms are particularly useful for maximum power point tracking (MPPT) applications, because they can adapt to changes in operating conditions and effectively handle partial shading conditions. However, metaheuristic algorithms also have some limitations that need to be addressed to make them suitable for MPPT applications. The problems associated with metaheuristic algorithm-based MPPT applications include being trapped in local optima, slow convergence speed, shading condition variability, computational complexity and robustness. These problems lead to reduced efficiency in MPPT applications. In the literature, the solution of the aforementioned problems is partially addressed and some of them are solved via an additional irradiation sensor. The motivation of this study is to develop a control algorithm that covers all problems that have been partially solved in the literature and includes an original re-initialization modeling method in accordance with visual programing concept, without using any additional radiation sensor. The proposed control algorithm has the flexibility to be easily adapted to other metaheuristic algorithms and does not require any radiation sensors. The re-initialization model created via Matlab/Simulink and "Embedded Coder Support Package for TI C2000 Processors" allows easy tracking of the global maximum power point (GMPP) by detecting variable radiation conditions. The proposed model was implemented on the Cuckoo Search Algorithm (CSA) and verified through experimental studies carried out with a TI-TMS320f28069 microcontroller and PV emulator. The experimental results confirm that issue 1 is solved with 100%, issue 2 is solved with 99.5%, issue 3 is solved with 99.84%, and issue 4 is solved with 100% MPPT efficiency. [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]

High performance computing (HPC) electromagnetic (EM) emulators are used to simulate real-time EM wave interactions between numerous radar targets. Radar Cross Section (RCS) data stores reflection profiles of radar targets as a table; however, the needed storage size increases with frequency sampling density, aspect angle sampling density, and number of target types. The large quantity of data often exceeds storage capability and limits manipulation and representation feasibility. The spherical harmonic based monostatic anisotropic point scatterer model is proposed for HPC EM simulations where scattering responses can be computed with finite impulse response (FIR) filters. An efficient algorithm constructing this model with large scale RCS data is discussed. The scatterer position and the reflection profile of each scatterer are solved using least squares methods and particle swarm optimization (PSO). In addition, the function evaluations in PSO are accelerated by taking advantage of the matrix structure, making the algorithm 22 times faster [ABSTRACT FROM AUTHOR]

This paper proposes an improved Wind Turbine Emulator (WTE) structure using Sliding Mode Control (SMC) technique based on the Two-Phase Induction Motor (TPIM) for software simulation laboratory purposes. Since the power electronic converters application with various control strategies in the AC machine drive field facilitates the wind turbine characteristics imitation, an advanced SVM controlled-2ϕ-inverter is adopted. Considering the asymmetry between the TPIM main and auxiliary windings, mathematical equations were studied allowing control of the speed and the torque under different conditions. The 2-phase-inverter is controlled through an appropriate adjustment of four space voltage vectors. In order to enhance the speed and flux regulation loops and to increase the system robustness against the motor parameters variation and the wind sudden fluctuations, the SMC theory, which contains the adaptive rotor flux observer is used here to develop a control law that governs the system to track the reference speed set by the WT mathematical model to obtain the desired output. Several parameters, such as electromagnetic torque, rotor fluxes, stator currents, and rotor speed, were analyzed. Static and dynamic characteristics and the WTE transient and steady-state responses are satisfactorily reproduced by the proposed emulator scheme. [ABSTRACT FROM AUTHOR]

This paper seeks to use eye-tracking emulation software to understand and forecast the human responses to signage and directional cues in Massachusetts, USA. Using eye-tracking emulation software, this study measured the unconscious visual responses people are projected to have to designs and layouts in new built environments, focusing on what makes streets most walkable. The study found differences between the way the brain takes in conventional automobile-oriented residential developments versus new urbanist layouts, with the former lacking key fixation points. The study's discoveries explain why new urbanist layouts promote walking effortlessly and conventional automobile-oriented residential developments cannot. [ABSTRACT FROM AUTHOR]

Simulation and emulation of electric motor faults are particularly useful to study, test, and evaluate the motor condition monitoring products based on the motor current signature and vibration analysis. Many researchers have proposed and developed several DAQ & computer-based simulation tools. This paper aims to develop a system to simulate and emulate the maximum types of faults which can occur in a three-phase induction motor. In this paper, an embedded system based induction motor fault simulator and the emulator is proposed and developed. The focus of this paper is to model the induction motor faults mathematically based on the motor current signature and vibration frequency spectrum, which are theoretically derived and proved by the early generation researchers and engineers. The novelty of the proposed approach is a cost-effective, portable, and handy tool for field evaluation of the motor condition monitoring products. This paper presents the hardware and software development for making a stand-alone motor faults simulator and emulator. The system employs a powerful high-end and powerful embedded system INTEL ATOM Minnow Board, which is built around E3826, Dual Core, and 1.46 GHz processor. A dedicated 4-channel Direct Digital Synthesizer (DDS) with 12-bit resolution along with software generates all three-phase induction motor faults for both MCSA and vibration analysis. The simulated and emulated faults signals are compared and evaluated against the Standards with the help of a digital storage oscilloscope. [ABSTRACT FROM AUTHOR]

Computer models are used as replacements for physical experiments in a large variety of applications. Nevertheless, direct use of the computer model for the ultimate scientific objective is often limited by the complexity and cost of the model. Gaussian process regression has been the almost ubiquitous choice for a fast statistical emulator for such a computer model, due to its flexible form and analytical expressions for measures of predictive uncertainty. However, even this statistical emulator can be computationally intractable for large designs, due to computing time increasing with the cube of the design size. Multiple methods have been proposed for addressing this problem. We discuss several of them, and compare their predictive and computational performance in several scenarios. We propose solving this problem using a new method, adaptive design and analysis via partitioning trees (ADAPT). The new approach is motivated by the idea that most computer models are only complex in particular regions of the input space. By taking a data-adaptive approach to the development of a design, and choosing to partition the space in the regions of highest variability, we obtain a higher density of points in these regions and hence accurate prediction. Supplemental files for this article are available online. [ABSTRACT FROM AUTHOR]

Binary translators, which translate the binary executables from one instruction set to another, are useful tools. Indirect branches are one of the key factors that affect the efficiency of binary translators. In the previous research, our lab developed an LLVM-based binary translation framework, called Rabbit. Rabbit introduces novel optimisations: platform-dependent hyperchaining and platform-independent hyperchaining for improving the emulation of the indirect branch instructions. Indirect branch instructions may have several destinations, and these destinations are not known until runtime. Both platform-independent and platform-dependent hyperchaining establish a search table for each indirect branch instruction to record the visited branch destinations at runtime. In this work, we focus on the translation from AArch64 binary to RISC-V binary and further develop the profile-guided optimisation for indirect branch, which collects runtime information, including branch destinations and execution frequency of each destination for each indirect branch instruction, and then use the information to improve hyperchaining (i.e. accelerate the process of finding the branch destination). The profile-guided optimisation can be divided to profile-guided platform-independent hyperchaining and profile-guided platform-dependent hyperchaining. We finally use SPEC CPU 2006 CINT benchmark to evaluate the optimisations. The experiment results indicate that compared with (1) no chaining, (2) platform-independent hyperchaining and (3) platform-dependent hyperchaining, profile-guided platform-independent hyperchaining provides 1.123×, 1.066× and 1.098× speedup respectively. Similarly, profile-guided platform-dependent hyperchaining achieves 1.106×, 1.047× and 1.083× speedup with respect to the above three configurations, respectively. [ABSTRACT FROM AUTHOR]

The Gaussian process is a standard tool for building emulators for both deterministic and stochastic computer experiments. However, application of Gaussian process models is greatly limited in practice, particularly for large-scale and many-input computer experiments that have become typical. We propose a multiresolution functional ANOVA (MRFA) model as a computationally feasible emulation alternative. More generally, this model can be used for large-scale and many-input nonlinear regression problems. An overlapping group lasso approach is used for estimation, ensuring computational feasibility in a large-scale and many-input setting. New results on consistency and inference for the (potentially overlapping) group lasso in a high-dimensional setting are developed and applied to the proposed MRFA model. Importantly, these results allow us to quantify the uncertainty in our predictions. Numerical examples demonstrate that the proposed model enjoys marked computational advantages. Data capabilities, in terms of both sample size and dimension, meet or exceed best available emulation tools while meeting or exceeding emulation accuracy. for this article are available online. [ABSTRACT FROM AUTHOR]

In order to effectively use a memristor in analog circuits, its memristance should be adjusted to a desired value between its limits. Since the maximum and minimum required memristance typically varies considerably between different types of memristors, it is almost impossible to tune the resistance of each memristor based on a reference resistor. Which is mostly done using some programmer circuits. Moreover, those programming strategies involving pulses are time-consuming and they impose high hardware headroom. In this paper, a novel CMOS circuit is presented for programming memristors. A Wheatstone bridge circuit is used to measure the current memristance, while the programming current is flowing through the device. Using such an approach reading the state and its adjustment are done simultaneously, which reduces the programming latency. In the proposed method, instead of tuning the memristance, the state of the memristor will be set to the desired value, which is proportional to a control voltage. The low programming latency, six-bit accuracy, and use of a simple circuit for programming, are the main advantages of our solution. The proposed circuit is designed and laid out in 0.35 µm CMOS technology and takes 0.0273mm2. Furthermore, the proposed approach is applied to a memristor emulator to demonstrate its correct operation in real applications. [ABSTRACT FROM AUTHOR]

An atmospheric release of hazardous material, whether accidental or intentional, can be catastrophic for those in the path of the plume. Predicting the path of a plume based on characteristics of the release (location, amount, and duration) and meteorological conditions is an active research area highly relevant for emergency and long-term response to these releases. As a result, researchers have developed particle dispersion simulators to provide plume path predictions that incorporate release characteristics and meteorological conditions. However, since release characteristics and meteorological conditions are often unknown, the inverse problem is of great interest, that is, based on all the observations of the plume so far, what can be inferred about the release characteristics? This is the question we seek to answer using plume observations from a controlled release at the Diablo Canyon Nuclear Power Plant in Central California. With access to a large number of evaluations of a computationally expensive particle dispersion simulator that includes continuous and categorical inputs and spatio-temporal output, building a fast statistical surrogate model (or emulator) presents many statistical challenges, but is an essential tool for inverse modeling and sensitivity analysis. We achieve accurate emulation using Bayesian adaptive splines to model weights on empirical orthogonal functions. We use this emulator as well as appropriately identifiable simulator discrepancy and observational error models to calibrate the simulator, thus finding a posterior distribution of characteristics of the release. Since the release was controlled, these characteristics are known, making it possible to compare our findings to the truth. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

This paper reports the emergence of bi-stability in a third-order memristive Wien-bridge oscillator. By transforming the parallel RC feedback network into a series resistor and capacitor (RC) feedback network in the previous non-ideal voltage-controlled memristor emulator, an improved memristor emulator is proposed and its fingerprints are exhibited. And then through introducing the proposed emulator to replace the parallel resistor in the conventional Wien-bridge oscillator, an improved memristor-based third-order Wien-bridge oscillator is constructed. Based on its mathematical model, the initial condition-dependent dynamical behaviour is revealed by theoretical analyses and numerical simulations and further verified by hardware experiments, from which the emergence of bi-stability in the memristive oscillator is well demonstrated. [ABSTRACT FROM AUTHOR]

The recent accelerated growth in the computing power has generated popularization of experimentation with dynamic computer models in various physical and engineering applications. Despite the extensive statistical research in computer experiments, most of the focus had been on the theoretical and algorithmic innovations for the design and analysis of computer models with scalar responses. In this article, we propose a computationally efficient statistical emulator for a large-scale dynamic computer simulator (i.e., simulator which gives time series outputs). The main idea is to first find a good local neighborhood for every input location, and then emulate the simulator output via a singular value decomposition (SVD) based Gaussian process (GP) model. We develop a new design criterion for sequentially finding this local neighborhood set of training points. Several test functions and a real-life application have been used to demonstrate the performance of the proposed approach over a naive method of choosing local neighborhood set using the Euclidean distance among design points. The supplementary material, which contains proof of the theoretical results, detailed algorithms, additional simulation results, and R codes, are available online. [ABSTRACT FROM AUTHOR]

We consider Bayesian design of experiments problems in which we maximize the prior expectation of a utility function over a set of permutations, for example, when sequencing a number of tasks to perform. When the number of tasks is large and the expected utility is expensive to compute, it may be unreasonable or infeasible to evaluate the expected utility of all permutations. We propose an approach to emulate the expected utility using a surrogate function based on a parametric probabilistic model for permutations. The surrogate function is fitted by maximizing the correlation with the expected utility over a set of training points. We propose a suitable transformation of the expected utility to improve the fit. We provide results linking the correlation between the two functions and the number of expected utility evaluations to undertake. The approach is applied to the sequencing of reliability growth tasks in the development of hardware systems, in which there are a large number of potential tasks to perform and engineers are interested in meeting a reliability target subject to minimizing costs and time. An illustrative example shows how the approach can be used and a simulation study demonstrates the performance of the approach more generally. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

A robust fault-tolerant control scheme using an accurate and robustly identified model of a system operating in a closed loop is proposed. A robust identification of the system and the associated Kalman filter is proposed by the novel use of an emulator, which is a transfer function connected to the input, output or both, to mimic the likely operating scenarios, both normal and abnormal. From the emulator-generated data, a set of perturbed models are generated, which plays a crucial role in ensuring robustness of the identified model and subsequently that of the controller. The prediction error method is employed to identify the perturbed models and a robust optimal model is obtained as a best fit to the perturbed models. A robust Kalman filter-based state feedback controller is obtained fromthe parameterisation of all stabilising controllers generated using the emulator-perturbed models. It is shown theoretically that the proposed robust controller designed using the emulator-perturbed robustly identified model is superior to the conventional robust controller designed from the identified nominal model, and a significant improvement in robustness is confirmed using illustrative examples. A fault-tolerant control system is developed by exploiting the key properties of the freely available Kalman filter. [ABSTRACT FROM AUTHOR]

Traffic flow count data in networks arise in many applications, such as automobile or aviation transportation, certain directed social network contexts, and Internet studies. Using an example of Internet browser traffic flow through site-segments of an international news website, we present Bayesian analyses of two linked classes of models which, in tandem, allow fast, scalable, and interpretable Bayesian inference. We first develop flexible state-space models for streaming count data, able to adaptively characterize and quantify network dynamics efficiently in real-time. We then use these models as emulators of more structured, time-varying gravity models that allow formal dissection of network dynamics. This yields interpretable inferences on traffic flow characteristics, and on dynamics in interactions among network nodes. Bayesian monitoring theory defines a strategy for sequential model assessment and adaptation in cases when network flow data deviate from model-based predictions. Exploratory and sequential monitoring analyses of evolving traffic on a network of web site-segments in e-commerce demonstrate the utility of this coupled Bayesian emulation approach to analysis of streaming network count data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Piping erosion is one of the major causes of failure of flood defences. The occurrence of this failure mechanism is difficult to predict and it can be triggered during a flood event. The inclusion of hard structures under flood defences will change the probability of occurrence of this erosion process. The present study aimed at understanding the effect of an embedded sewer pipe under the flood defence foundation in its safety assessment for piping erosion failure. This was done by setting a probabilistic analysis framework based on emulation of finite element models of porous media flow and the fictitious permeability approach. The effects of size and location of the sewer pipe were evaluated via a deterministic safety factor approach. Afterwards, emulators of the most safe and unsafe models were trained and used for probabilistic assessments. The results showed that the embedment of a sewer pipe in the flood defence foundation has a significant effect on its safety. The magnitude of the effect is highly dependent on the size and location of the sewer pipe. Furthermore, the foundation permeability uncertainty shows a conditional effect with respect to the sewer pipe size and location. [ABSTRACT FROM AUTHOR]

Sequential experiments composed of initial experiments and follow-up experiments are widely adopted for economical computer emulations. Many kinds of Latin hypercube designs with good space-filling properties have been proposed for designing the initial computer experiments. However, little work based on Latin hypercubes has focused on the design of the follow-up experiments. Although some constructions of nested Latin hypercube designs can be adapted to sequential designs, the size of the follow-up experiments needs to be a multiple of that of the initial experiments. In this article, a general method for constructing sequential designs of flexible size is proposed, which allows the combined designs to have good one-dimensional space-filling properties. Moreover, the sampling properties and a type of central limit theorem are derived for these designs. Several improvements of these designs are made to achieve better space-filling properties. Simulations are carried out to verify the theoretical results. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Mathematical models are frequently used to explore physical systems, but can be computationally expensive to evaluate. In such settings, an emulator is used as a surrogate. In this work, we propose a basis-function approach for computer model emulation. To combine field observations with a collection of runs from the numerical model, we use the proposed emulator within the Kennedy-O’Hagan framework of model calibration. A novel feature of the approach is the use of an over-specified set of basis functions where number of bases used and their inclusion probabilities are treated as unknown quantities. The new approach is found to have smaller predictive uncertainty and computational efficiency than the standard Gaussian process approach to emulation and calibration. Along with several simulation examples focusing on different model characteristics, we also use the method to analyze a dataset on laboratory experiments related to astrophysics. [ABSTRACT FROM PUBLISHER]

This paper presents four new topologies for emulating floating immittance functions. Each circuit uses two or three current-feedback operational-amplifiers (CFOAs) and three passive elements. The proposed topologies can emulate positive/negative lossless and lossy floating inductances, and positive/negative capacitance, resistance and inductance multipliers in addition to floating frequency-dependent positive and negative resistances. The functionality of the proposed circuits is verified using the Advanced Design System software and the AD844 CFOA. The simulation results are in excellent agreement with the theoretical calculations. [ABSTRACT FROM PUBLISHER]

Recent implementations of local approximate Gaussian process models have pushed computational boundaries for nonlinear, nonparametric prediction problems, particularly when deployed as emulators for computer experiments. Their flavor of spatially independent computation accommodates massive parallelization, meaning that they can handle designs two or more orders of magnitude larger than previously. However, accomplishing that feat can still require massive computational horsepower. Here we aim to ease that burden. We study how predictive variance is reduced as local designs are built up for prediction. We then observe how the exhaustive and discrete nature of an important search subroutine involved in building such local designs may be overly conservative. Rather, we suggest that searching the space radially, that is, continuously along rays emanating from the predictive location of interest, is a far thriftier alternative. Our empirical work demonstrates that ray-based search yields predictors with accuracy comparable to exhaustive search, but in a fraction of the time—for many problems bringing a supercomputer implementation back onto the desktop. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

A nearly accurate solar photovoltaic emulator using DC-DC buck converter is proposed. A mathematical model developed using MATLAB/SIMULINK (The MathWorks, Natick, Massachusetts, USA) serving as reference is built for emulating solar photovoltaic characteristics. Hardware implementation is done using a dSPACE ds1104 controller board (dSPACE GmbH, Paderborn, Germany), and a closed-loop control system is constructed using proportional integral (PI) controller. The proposed emulator has the following characteristics:(1) faster dynamic response with easy interfacing, (2) the advantage of real-time control, (3) a DC-DC buck converter with higher bandwidth, (4) highly stable output with lower response time, and (5) lower output voltage and current ripple. Experimentations are done under different operating conditions, and the results obtained are presented. Further, to demonstrate the capability of the proposed emulator, maximum power point tracking using the hill-climbing method is tested on a DC-DC boost converter interfaced to the solar photovoltaic emulator. [ABSTRACT FROM PUBLISHER]

Spectrum sensing is the main function of cognitive radio (CR), which enables the CR users to detect the spectrum holes. Inherent characteristics of CR have imposed some serious threats to the networks. One of the common threats in CR network is primary user emulation attack (PUEA). In this particular type of attack, some malicious users try to imitate primary signal characteristics and defraud CR users to prevent them from accessing the spectrum holes. Therefore, an effective defense strategy is extremely important for robust collaborative spectrum sensing (CSS). The current study introduces a new CSS scheme in the presence of an intelligent PUEA, called attack-aware CSS (ACSS), which is aware of spectrum holes and actually co-located with the licensed primary user (PU) and transmits with the same power level in a way that CR users are not easily able to differentiate between received signal from PU and PUEA. The idea is based on attack strength estimation, where the attack strength is defined as the channel occupancy rate of malicious PUEA which equals to the probability that the malicious emulator occupies a specific spectrum hole. The proposed approach estimates the attack strength and innovatively applies in Neyman–Pearson or likelihood ratio test to improve collaborative sensing performance. Simulation results are provided to indicate the superiority of the proposed ACSS method against PUEA compared with the conventional method. [ABSTRACT FROM PUBLISHER]

Constrained blackbox optimization is a difficult problem, with most approaches coming from the mathematical programming literature. The statistical literature is sparse, especially in addressing problems with nontrivial constraints. This situation is unfortunate because statistical methods have many attractive properties: global scope, handling noisy objectives, sensitivity analysis, and so forth. To narrow that gap, we propose a combination of response surface modeling, expected improvement, and the augmented Lagrangian numerical optimization framework. This hybrid approach allows the statistical model to think globally and the augmented Lagrangian to act locally. We focus on problems where the constraints are the primary bottleneck, requiring expensive simulation to evaluate and substantial modeling effort to map out. In that context, our hybridization presents a simple yet effective solution that allows existing objective-oriented statistical approaches, like those based on Gaussian process surrogates and expected improvement heuristics, to be applied to the constrained setting with minor modification. This work is motivated by a challenging, real-data benchmark problem from hydrology where, even with a simple linear objective function, learning a nontrivial valid region complicates the search for a global minimum. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Running complex computer models can be expensive in computer time, while learning about the relationships between input and output variables can be difficult. An emulator is a fast approximation to a computationally expensive model that can be used as a surrogate for the model, to quantify uncertainty or to improve process understanding. Here, we examine emulators based on singular value decompositions (SVDs) and use them to emulate global climate and vegetation fields, examining how these fields are affected by changes in the Earth's orbit. The vegetation field may be emulated directly from the orbital variables, but an appealing alternative is to relate it to emulations of the climate fields, which involves high-dimensional input and output. The SVDs radically reduce the dimensionality of the input and output spaces and are shown to clarify the relationships between them. The method could potentially be useful for any complex process with correlated, high-dimensional inputs and/or outputs. [ABSTRACT FROM PUBLISHER]

We provide a new approach to approximate emulation of large computer experiments. By focusing expressly on desirable properties of the predictive equations, we derive a family of local sequential design schemes that dynamically define the support of a Gaussian process predictor based on a local subset of the data. We further derive expressions for fast sequential updating of all needed quantities as the local designs are built up iteratively. Then we show how independent application of our local design strategy across the elements of a vast predictive grid facilitates a trivially parallel implementation. The end result is a global predictor able to take advantage of modern multicore architectures, providing a nonstationary modeling feature as a bonus. We demonstrate our method on two examples using designs with thousands of data points, and compare to the method of compactly supported covariances. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

Computer simulators of real-world processes are often computationally expensive and require many inputs. The problem of the computational expense can be handled using emulation technology; however, highly multidimensional input spaces may require more simulator runs to train and validate the emulator. We aim to reduce the dimensionality of the problem by screening the simulator’s inputs for nonlinear effects on the output rather than distinguishing between negligible and active effects. Our proposed method is built upon the elementary effects (EE) method for screening and uses a threshold value to separate the inputs with linear and nonlinear effects. The technique is simple to implement and acts in a sequential way to keep the number of simulator runs down to a minimum, while identifying the inputs that have nonlinear effects. The algorithm is applied on a set of simulated examples and a rabies disease simulator where we observe run savings ranging between 28% and 63% compared with the batch EE method. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Computer simulation has increasingly become popular for analysis of systems that cannot be feasibly changed because of costs or scale. This work proposes a method to construct anemulatorfor stochastic simulations by performing a designed experiment on the simulator and developing an emulative distribution. Existing emulators have focused on estimation of themeanof the simulation output, but this work presents an emulator for thedistributionof the output. This construction provides both an explicit distribution and a fast sampling scheme. Beyond the emulator description, this work demonstrates the emulator’s efficiency, that is, its convergence rate is the asymptotically optimal among all possible emulators using the same sample size (under certain conditions). An example of its practical use is demonstrated using a stochastic simulation of fracture mechanics. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

Whole brain emulation attempts to achieve software intelligence by copying the function of biological nervous systems into software. This paper aims at giving an overview of the ethical issues of the brain emulation approach, and analysing how they should affect responsible policy for developing the field. Animal emulations have uncertain moral status, and a principle of analogy is proposed for judging treatment of virtual animals. Various considerations of developing and using human brain emulations are discussed. [ABSTRACT FROM AUTHOR]

Computer experiments are widely used in scientific research to study and predict the behavior of complex systems, which often have responses consisting of a set of nonstationary outputs. The computational cost of simulations at high resolution often is expensive and impractical for parametric studies at different input values. In this article, we develop a Bayesian treed multivariate Gaussian process (BTMGP) as an extension of the Bayesian treed Gaussian process (BTGP) to model the cross-covariance function and the nonstationarity of the multivariate output. We facilitate the computational complexity of the Markov chain Monte Carlo sampler by choosing appropriately the covariance function and prior distributions. Based on the BTMGP, we develop a sequential design of experiment for the input space and construct an emulator. We demonstrate the use of the proposed method in test cases and compare it with alternative approaches. We also apply the sequential sampling technique and BTMGP to model the multiphase flow in a full scale regenerator of a carbon capture unit. [ABSTRACT FROM AUTHOR]

Climate model development, testing, and analysis involve running the model extensively to tune the subgrid- scale parameters that provide closure to the system. This process demands substantial time and computational resources even for typical spatial resolutions and becomes in feasibly expensive for high-resolution studies. This paper presents alternative, computationally feasible methods to emulate the simulations within acceptable error bounds. This strategy can be easily implemented to obtain an ensemble of model runs. The paper outlines three approximation strategies: (1) interpolation with Lagrange basis functions, (2) least-squares (LS) approximation, and (3) interpolation with radial basis functions. As a proof of concept, a suite of relevant physical quantities are evaluated at unknown grid points of parameters, space, and time. The values obtained by emulation are compared against the simulated values to check the feasibility of the method. The advantages and shortcomings of the above-mentioned approximation schemes are discussed, including the savings of time and computational resources. [ABSTRACT FROM AUTHOR]

Radiation hydrodynamics and radiative shocks are of fundamental interest in the high-energy-density physics research due to their importance in understanding astrophysical phenomena such as supernovae. In the laboratory, experiments can produce shocks with fundamentally similar physics on reduced scales. However, the cost and time constraints of the experiment necessitate use of a computer algorithm to generate a reasonable number of outputs for making valid inference. We focus on modeling emulators that can efficiently assimilate these two sources of information accounting for their intrinsic differences. The goal is to learn how to predict the breakout time of the shock given the information on associated parameters such as pressure and energy. Under the framework of the Kennedy–O’Hagan model, we introduce an emulator based on adaptive splines. Depending on the preference of having an interpolator for the computer code output or a computationally fast model, a couple of different variants are proposed. Those choices are shown to perform better than the conventional Gaussian-process-based emulator and a few other choices of nonstationary models. For the shock experiment dataset, a number of features related to computer model validation such as using interpolator, necessity of discrepancy function, or accounting for experimental heterogeneity are discussed, implemented, and validated for the current dataset. In addition to the typical Gaussian measurement error for real data, we consider alternative specifications suitable to incorporate noninformativeness in error distributions, more in agreement with the current experiment. Comparative diagnostics, to highlight the effect of measurement error model on predictive uncertainty, are also presented. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]

The very high run times associated with microsimulation and agent based models of travel demand preclude their application in real time policy development sessions in which discussions require fast turnaround times of model simulation results. This paper suggests and explores the use of emulators to increase the possibilities of using large scale simulation models in real time policy development sessions. An emulator is a statistical approximation of a large scale model, depicting the relationship between a set of input variables and an output variable of a large scale model. To illustrate the approach, an emulator is built for the Albatross model system with special reference to its daily distance traveled per person performance indicator. Results are satisfactory and show that simple main effects plus first order interaction effects of regression model can approximate daily distance traveled per person, simulated by the considerably more complex large scale Albatross model system, very accurately. The paper also examines the impact of the number of simulation runs on the performance of the emulator. Results suggest that the accuracy of the emulator improves, albeit in a non-linear fashion, with an increasing number of simulation runs. Implications and avenues of future research are discussed. [ABSTRACT FROM AUTHOR]

The Gaussian process regression model is a popular type of “emulator” used as a fast surrogate for computationally expensive simulators (deterministic computer models). For simulators with multivariate output, common practice is to specify a separable covariance structure for the Gaussian process. Though computationally convenient, this can be too restrictive, leading to poor performance of the emulator, particularly when the different simulator outputs represent different physical quantities. Also, treating the simulator outputs as independent can lead to inappropriate representations of joint uncertainty. We develop nonseparable covariance structures for Gaussian process emulators, based on the linear model of coregionalization and convolution methods. Using two case studies, we compare the performance of these covariance structures both with standard separable covariance structures and with emulators that assume independence between the outputs. In each case study, we find that only emulators with nonseparable covariances structures have sufficient flexibility both to give good predictions and to represent joint uncertainty about the simulator outputs appropriately. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

Neuroemulation is the art and science of using a neural network model to replicate the external behaviour of some other model or component of a model. It is an independent activity that is distinct from neural network-based simulation. Neuroemulation has become a recognized and established sub-discipline in many spheres of study, but remains poorly defined in the field of water resources research. Its many potential benefits have not yet been adequately recognized or established. Lack of recognition can in part be attributed to difficulties involved in identifying, collating and synthesizing published studies on neuroemulation: query-based searching of a publications database fails to identify papers concerned with a field of study, for which no agreed conceptual and/or terminological framework as yet exists. Therefore, in this paper, we provide a first attempt at defining such a framework for use in water resources investigations. We identify eight key benefits offered by neuroemulation and exemplify current activities with relevant examples taken from published research in the field. The concluding section highlights a number of strategic research directions related to developing the identified potential of neuroemulator applications for water resources modelling. Editor D. Koutsoyiannis Citation Abrahart, R.J., Mount, N.J. and Shamseldin, A.Y., 2012. Neuroemulation: definition and key benefits for water resources research. Hydrological Sciences Journal, 57 (3), 407–423. [ABSTRACT FROM AUTHOR]

A single-index model (SIM) provides for parsimonious multidimensional nonlinear regression by combining parametric (linear) projection with univariate nonparametric (nonlinear) regression models. We show that a particular Gaussian process (GP) formulation is simple to work with and ideal as an emulator for some types of computer experiment as it can outperform the canonical separable GP regression model commonly used in this setting. Our contribution focuses on drastically simplifying, reinterpreting, and then generalizing a recently proposed fully Bayesian GP-SIM combination. Favorable performance is illustrated on synthetic data and a real-data computer experiment. Two R packages, both released on CRAN, have been augmented to facilitate inference under our proposed model(s). [ABSTRACT FROM AUTHOR]

The article explores the development of library web sites optimized for mobile devices. Topics to be covered will include beginning the process of developing for mobile devices, assessing project goals, text-based contact with library staff, and mobile device emulators. [ABSTRACT FROM AUTHOR]

Objective: To assess the effectiveness and acceptability of microswitch technology and a keyboard emulator to enable three participants with extensive neuro-motor disabilities to write words. Method: In Study I, two participants triggered an automatic scanning keyboard and selected/wrote letters via a small sliding movement of their hand(s) activating a touch/pressure panel (microswitch). In Study II, a third participant used the sliding movement and panel and a vocalization response with a voice-detecting microswitch. The sliding movement allowed her to light up the keyboard and select the letters and the vocalization to perform the scanning. Results: Participants showed a better performance (shorter writing time) or an equally effective but less tiring performance with the new microswitch technology and response(s). They also preferred using this technology, and social validation ratings favoured such technology over previous solutions. Conclusion: The aforementioned technology may be useful to enable persons with extensive neuro-motor disabilities to write successfully. [ABSTRACT FROM AUTHOR]

A low-cost statistical polarization mode dispersion/polarization dependent loss emulator is presented in this article. The emulator was constructed by concatenating 15 highly birefringence optical-fiber segments and randomly varying the mode coupling between them by rotating the polarization state. The impact of polarization effects on polarization division multiplexing transmission quality was measured. The designed polarization mode dispersion/polarization dependent loss emulator was applied to mimic the polarization effects of real optical-fiber links. [ABSTRACT FROM AUTHOR]

The authors propose an interesting alternative that combines the augmented Langrangian with Gaussian process (GP) emulator and expected improvement. They cite the usefulness of the augmented Langrangian as it allows the algorithm to start from an infeasible point and provides information on how far away the current point is from the boundary. They show that simulated annealing does not perform as well as the proposed methods since it requires more time of function evaluations.

The thermosphere-ionosphere electrodynamics general circulation model (TIE-GCM) of the upper at- mosphere has a number of features that are a challenge to standard approaches to emulation, including a long run time, multivariate output, periodicity, and strong constraints on the interrelationship between inputs and outputs. These kinds of features are not unusual in models of complex systems. We show how they can be handled in an emulator and demonstrate the use of the outer product emulator for efficient calculation, with an emphasis on predictive diagnostics for model choice and model validation. We use our emulator to "verify" the underlying computer code and to quantify our qualitative physical understanding. [ABSTRACT FROM AUTHOR]

Mathematical models, usually implemented in computer programs known as simulators, are widely used in all areas of science and technology to represent complex real-world phenomena. Simulators are often so complex that they take appreciable amounts of computer time or other resources to run. In this context, a methodology has been developed based on building a statistical representation of the simulator, known as an emulator. The principal approach to building emulators uses Gaussian processes. This work presents some diagnostics to validate and assess the adequacy of a Gaussian process emulator as surrogate for the simulator. These diagnostics are based on comparisons between simulator outputs and Gaussian process emulator outputs for some test data, known as validation data, defined by a sample of simulator runs not used to build the emulator. Our diagnostics take care to account for correlation between the validation data. To illustrate a validation procedure, we apply these diagnostics to two different data Sets. [ABSTRACT FROM AUTHOR]

We consider the problem of designing for complex high-dimensional computer models that can be evaluated at different levels of accuracy. Ordinarily, this requires performing many expensive evaluations of the most accurate version of the computer model to obtain a reasonable coverage of the design space. In some cases, it is possible to supplement the information from the accurate model evaluations with a large number of evaluations of a cheap, approximate version of the computer model to enable a more informed design choice. We describe an approach that combines the information from both the approximate model and the accurate model into a single multiscale emulator for the computer model. We then propose a design strategy for selecting a small number of expensive evaluations of the accurate computer model based on our multiscale emulator and a decomposition of the input parameter space. We illustrate our methodology with an example concerning a computer simulation of a hydrocarbon reservoir. [ABSTRACT FROM AUTHOR]

Risk assessment of rare natural hazards, such as large volcanic block and ash or pyroclastic flows, is addressed. Assessment is approached through a combination of computer modeling, statistical modeling, and extreme-event probability computation. A computer model of the natural hazard is used to provide the needed extrapolation to unseen parts of the hazard space. Statistical modeling of the available data is needed to determine the initializing distribution for exercising the computer model. In dealing with rare events, direct simulations involving the computer model are prohibitively expensive. The solution instead requires a combination of adaptive design of computer model approximations (emulators) and rare event simulation. The techniques that are developed for risk assessment are illustrated on a test-bed example involving volcanic flow. [ABSTRACT FROM AUTHOR]

One of the challenges with emulating the response of a multivariate function to its inputs is the quantity of data that must be assimilated, which is the product of the number of model evaluations and the number of outputs. This article shows how even large calculations can be made tractable. It is already appreciated that gains can be made when the emulator residual covariance function is treated as separable in the model-inputs and model-outputs. Here, an additional simplification on the structure of the regressors in the emulator mean function allows very substantial further gains. The result is that it is now possible to emulate rapidly--on a desktop computer--models with hundreds of evaluations and hundreds of outputs. This is demonstrated through calculating costs in floating-point operations, and in an illustration. Even larger sets of outputs are possible if they have additional structure, for example, spatial-temporal. [ABSTRACT FROM AUTHOR]

Recent advances in artificial neural networks (ANNs) have led to the design and construction of neuroarchitectures as simulator and emulators of a variety of problems in science and engineering. Such problems include pattern recognition, prediction, optimization, associative memory, and control of dynamic systems. This paper offers an analytical overview of the most successful design, implementation, and application of neuroarchitectures as neurosimulators and neuroemulators. It also outlines historical notes on the formulation of basic biological neuron, artificial computational models, network architectures, and learning processes of the most common ANN; describes and analyzes neurosimulation on parallel architecture both in software and hardware (neurohardware); presents the simulation of ANNs on parallel architectures; gives a brief introduction of ANNs in vector microprocessor systems; and presents ANNs in terms of the "new technologies". Specifically, it discusses cellular computing, cellular neural networks (CNNs), a new proposition for unsupervised neural networks (UNNs), and pulse coupled neural networks (PCNNs). [ABSTRACT FROM AUTHOR]

Presented in this study is an investigation of the real-time performance of a laboratory-scale model of wind turbine emulator. Developed to provide a cost-effective experimental facility for demonstrating and validating wind energy conversion systems at new wind territories, the emulator comprises a software component with a hardware interface of a motor-driven wound rotor induction generator. A real-life wind speed profile is implemented on the model to investigate its performance under a near-real-world scenario and, some operational characteristics of real wind energy conversion systems are exactly replicated by the emulator in the course of the experimentation. The emulator could therefore be considered suitable for use as a laboratory level testbed for the demonstration and validation of wind energy conversion systems in the real-time. [ABSTRACT FROM AUTHOR]