Showing papers on "Surrogate model published in 2022"

Predicting elemental stiffness matrix of FG nanoplates using Gaussian Process Regression based surrogate model in framework of layerwise model

Abstract: The accuracy of predicting the behaviour of structure using finite element (FE) depends widely on the precision of the evaluation of the stiffness matrix. In the present article, an attempt has been made to evaluate the stiffness matrix of functionally graded (FG) nanoplate using Gaussian process regression (GPR) based surrogate model in the framework of the layerwise theory. The stiffness matrix comprises various matrix terms corresponding to the membrane, membrane-bending, bending-membrane, and bending and shear. Following two different methodologies are adopted for predicting the stiffness matrix at the elemental level, one in which the final elemental stiffness matrix is evaluated, and the second one in which all the matrix terms as stated are evaluated separately using the GPR surrogate model and then are added to get the final stiffness matrix at the elemental level. The effectiveness of both approaches has been worked out by comparing the present results with those available in the literature. Both the proposed methodologies can predict the behaviour of FG nanoplates with good accuracy. However, the second one is found to be outstanding.

Variable surrogate model-based particle swarm optimization for high-dimensional expensive problems

Abstract: Abstract Many industrial applications require time-consuming and resource-intensive evaluations of suitable solutions within very limited time frames. Therefore, many surrogate-assisted evaluation algorithms (SAEAs) have been widely used to optimize expensive problems. However, due to the curse of dimensionality and its implications, scaling SAEAs to high-dimensional expensive problems is still challenging. This paper proposes a variable surrogate model-based particle swarm optimization (called VSMPSO) to meet this challenge and extends it to solve 200-dimensional problems. Specifically, a single surrogate model constructed by simple random sampling is taken to explore different promising areas in different iterations. Moreover, a variable model management strategy is used to better utilize the current global model and accelerate the convergence rate of the optimizer. In addition, the strategy can be applied to any SAEA irrespective of the surrogate model used. To control the trade-off between optimization results and optimization time consumption of SAEAs, we consider fitness value and running time as a bi-objective problem. Applying the proposed approach to a benchmark test suite of dimensions ranging from 30 to 200 and comparisons with four state-of-the-art algorithms show that the proposed VSMPSO achieves high-quality solutions and computational efficiency for high-dimensional problems.

An adaptive surrogate model to structural reliability analysis using deep neural network

Abstract: This article introduces a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN). In this paradigm, initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF). More important points on the boundary of limit state function (LSF) and their vicinities are subsequently added relied on the surrogate model to enhance its accuracy without any complex techniques. A threshold is proposed to switch from a globally predicting model to a locally one for the approximation of LSF by eradicating previously used unimportant and noise points. Accordingly, the surrogate model becomes more precise for the MCS-based failure probability assessment with only a small number of experiments. Six numerical examples with highly nonlinear properties, various distributions of random variables and multiple failure modes, namely three benchmark ones regarding explicit mathematical PFs and the others relating to finite element method (FEM)-programmed truss structures under free vibration, are examined to validate the present approach.

An adaptive surrogate model to structural reliability analysis using deep neural network

Abstract: This article introduces a simple and effective adaptive surrogate model to structural reliability analysis using deep neural network (DNN). In this paradigm, initial design of experiments (DoEs) are randomly selected from a given Monte Carlo Simulation (MCS) population to build the global approximate model of performance function (PF). More important points on the boundary of limit state function (LSF) and their vicinities are subsequently added relied on the surrogate model to enhance its accuracy without any complex techniques. A threshold is proposed to switch from a globally predicting model to a locally one for the approximation of LSF by eradicating previously used unimportant and noise points. Accordingly, the surrogate model becomes more precise for the MCS-based failure probability assessment with only a small number of experiments. Six numerical examples with highly nonlinear properties, various distributions of random variables and multiple failure modes, namely three benchmark ones regarding explicit mathematical PFs and the others relating to finite element method (FEM)-programmed truss structures under free vibration, are examined to validate the present approach.

Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models

Abstract: Abstract Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic efficiency. In this paper, we develop a system which combines reduced-order surrogate models with a novel data assimilation (DA) technique used to incorporate real-time observations from different physical spaces. We make use of local smooth surrogate functions which link the space of encoded system variables and the one of current observations to perform variational DA with a low computational cost. The new system, named generalised latent assimilation can benefit both the efficiency provided by the reduced-order modelling and the accuracy of data assimilation. A theoretical analysis of the difference between surrogate and original assimilation cost function is also provided in this paper where an upper bound, depending on the size of the local training set, is given. The new approach is tested on a high-dimensional (CFD) application of a two-phase liquid flow with non-linear observation operators that current Latent Assimilation methods can not handle. Numerical results demonstrate that the proposed assimilation approach can significantly improve the reconstruction and prediction accuracy of the deep learning surrogate model which is nearly 1000 times faster than the CFD simulation.

Using machine learning as a surrogate model for agent-based simulations

Abstract: In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Surrogate modelling, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM surrogate modelling in order to determine the approaches best suited as a surrogate for modelling the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process surrogates, currently the most commonly used method for the surrogate modelling of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for surrogate modelling, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.

On the Design of Complex EM Devices and Systems Through the System-by-Design Paradigm: A Framework for Dealing With the Computational Complexity

Abstract: The system-by-design (SbD) is an emerging engineering framework for the optimization-driven design of complex electromagnetic (EM) devices and systems. More specifically, the computational complexity of the design problem at hand is addressed by means of a suitable selection and integration of functional blocks comprising problem-dependent and computationally efficient modeling and analysis tools as well as reliable prediction and optimization strategies. Due to the suitable reformulation of the problem at hand as an optimization one, the profitable minimum-size coding of the degrees of freedom (DoFs), and the “smart” replacement of expensive full-wave (FW) simulators with proper surrogate models (SMs), which yield fast yet accurate predictions starting from minimum size/reduced CPU-costs training sets, a favorable “environment” for optimal exploitation of the features of global optimization tools in sampling wide/complex/nonlinear solution spaces is built. This research summary is then aimed at: 1) providing a comprehensive description of the SbD framework and of its pillar concepts and strategies; 2) giving useful guidelines for its successful customization and application to different EM design problems characterized by different levels of computational complexity; and 3) envisaging future trends and advances in this fascinating and high-interest (because of its relevant and topical industrial and commercial implications) topic. Representative benchmarks concerned with the synthesis of complex EM systems are presented to highlight advantages and potentialities as well as current limitations of the SbD paradigm.

Deep Belief Network Enabled Surrogate Modeling for Fast Preventive Control of Power System Transient Stability

Abstract: The widely used transient stability-constrained optimal power flow (TSC-OPF) method for power system preventive control is very time-consuming and thus not applicable for large-scale systems. This article proposes a new deep learning-enabled surrogate model that can significantly improve computational efficiency while maintaining high accuracy. To achieve that, the deep belief network (DBN) is strategically integrated with the reference-point-based nondominated sorting genetic algorithm (NSGA-III) to develop a new preventive control framework. The DBN allows us to identify the mapping relationship between the transient stability index and system operational features. The identified functional mapping relationship is further used as the surrogate to connect the DBN results with TSC-OPF for preventive control. The integrated NSGA-III and surrogate model enable the multiobjective optimization to consider various constraints and objectives, such as minimization of costs of generation dispatch cost and load shedding while maintaining the system stability. Extensive simulation results on several IEEE test systems show that the proposed method can achieve highly efficient control solutions and outperform other alternatives in terms of computational efficiency and economic benefits.

Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents

Abstract: Evacuation planning is important for reducing casualties in toxic gas leak incidents. However, most evacuation plans are too qualitative to be applied to unexpected practical situations. Here, we suggest an evacuation route proposal system based on a quantitative risk evaluation that provides the safest route for individual evacuees by predicting dynamic gas dispersion with a high accuracy and short calculation time. Detailed evacuation scenarios, including weather conditions, leak intensity, and evacuee information, were considered. The proposed system evaluates the quantitative risk in the affected area using a deep neural network surrogate model to determine optimal evacuation routes by integer programming. The surrogate model was trained using data from computational fluid dynamics simulations. A variational autoencoder was applied to extract the geometric features of the affected area. The predicted risk was combined with linearized integer programming to determine the optimal path in a predefined road network. A leak scenario of an ammonia gas pipeline in a petrochemical complex was used for the case study. The results show that the developed model offers the safest route within a few seconds with minimum risk. The developed model was applied to a sensitivity analysis to determine variable influences and safe shelter locations.

Surrogate-Assisted Autoencoder-Embedded Evolutionary Optimization Algorithm to Solve High-Dimensional Expensive Problems

Abstract: Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve computationally expensive problems with some success. However, traditional EAs are not suitable to deal with high-dimensional expensive problems (HEPs) with high-dimensional search space even if their fitness evaluations are assisted by surrogate models. The recently proposed autoencoder-embedded evolutionary optimization (AEO) framework is highly appropriate to deal with high-dimensional problems. This work aims to incorporate surrogate models into it to further boost its performance, thus resulting in surrogate-assisted AEO (SAEO). It proposes a novel model management strategy that can guarantee reasonable amounts of re-evaluations; hence, the accuracy of surrogate models can be enhanced via being updated with new evaluated samples. Moreover, to ensure enough data samples before constructing surrogates, a problem-dimensionality-dependent activation condition is developed for incorporating surrogates into the SAEO framework. SAEO is tested on seven commonly used benchmark functions and compared with state-of-the-art algorithms for HEPs. The experimental results show that SAEO can further enhance the performance of AEO on most cases and SAEO performs significantly better than other algorithms. Therefore, SAEO has great potential to deal with HEPs.

Accurate Modeling of Antenna Structures by Means of Domain Confinement and Pyramidal Deep Neural Networks

Abstract: The importance of surrogate modeling techniques has been gradually increasing in the design of antenna structures over the recent years. Perhaps the most important reason is a high cost of full-wave electromagnetic (EM) analysis of antenna systems. Although imperative in ensuring evaluation reliability, it entails considerable computational expenses. These are especially pronounced when carrying out EM-driven design tasks such as geometry parameter tuning or uncertainty quantification, both requiring repetitive simulations. Conducting some of the design procedures, e.g., global search or yield optimization, directly at the level of simulation models may be prohibitive. The use of fast replacement models (or surrogates) may alleviate these difficulties; yet, accurate modeling of antenna structures faces its own challenges. The two major obstacles are the curse of dimensionality, manifesting itself in a rapid growth of the number of training data samples necessary to render a reliable model (as a function of the number of antenna parameters) and high nonlinearity of antenna characteristics. Recently, the concept of performance-driven modeling has been introduced, where the modeling process is focused on a small region of the parameters’ space, which contains high-quality designs with respect to the considered performance figures. The most advanced variation in this class of methods is nested kriging, where both the model domain and the surrogate itself are constructed through kriging interpolation. Domain confinement is realized using a set of preoptimized reference designs and allows for significant improvement of the model predictive power while using a limited number of training data samples. In this work, the constrained modeling concept is coupled with a novel pyramidal deep regression network (PDRN) surrogate, which offers improved handling of highly nonlinear antenna responses. Three examples of microstrip antennas are used to demonstrate the advantages of constrained PDRN metamodels over the nested kriging surrogates with the (average) accuracy improved by a factor of 2 without increasing the training dataset cardinality.

A Microwave Filter Yield Optimization Method Based on Off-Line Surrogate Model-Assisted Evolutionary Algorithm

Abstract: Most existing microwave filter yield optimization methods target a small number of sensitive design variables (e.g., around 5). However, for many real-world cases, more than ten sensitive design variables need to be considered. Due to the complexity, yield optimization quality and efficiency become challenges. Hence, a new method, called yield optimization for filters based on the surrogate model-assisted evolutionary algorithm (YSMA), is proposed. The fundamental idea of YSMA is to construct a single high-accuracy surrogate model offline, which fully replaces electromagnetic (EM) simulations in the entire yield optimization process. Global optimization is then enabled to find designs with substantial yield improvement efficiently using the surrogate model. To reduce the number of necessary samples (i.e., EM simulations) while obtaining the required prediction accuracy, a customized machine learning technique is proposed. The performance of YSMA is demonstrated by two real-world examples with 11 and 14 design variables, respectively. Experimental results show the advantages of YSMA compared to the current dominant sequential online surrogate model-based local optimization methods.