Surrogate model ≈ Surrogate model
View article
Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization Open
In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs and varying fidelities. Typically, a comp…
View article
A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization Open
We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-ob…
View article
A review of surrogate models and their application to groundwater modeling Open
The spatially and temporally variable parameters and inputs to complex groundwater models typically result in long runtimes which hinder comprehensive calibration, sensitivity, and uncertainty analysis. Surrogate modeling aims to provide a…
View article
A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis Open
Structural finite-element analysis (FEA) has been widely used to study the biomechanics of human tissues and organs, as well as tissue–medical device interactions, and treatment strategies. However, patient-specific FEA models usually requ…
View article
Scalable Bayesian Optimization Using Deep Neural Networks Open
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for…
View article
Scalable Bayesian Optimization Using Deep Neural Networks Open
Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for…
View article
POLYNOMIAL-CHAOS-BASED KRIGING Open
International audience
View article
A Single-Loop Kriging Surrogate Modeling for Time-Dependent Reliability Analysis Open
Current surrogate modeling methods for time-dependent reliability analysis implement a double-loop procedure, with the computation of extreme value response in the outer loop and optimization in the inner loop. The computational effort of …
View article
Rare Event Estimation Using Polynomial-Chaos Kriging Open
Structural reliability analysis aims at computing the probability of failure of systems whose performance may be assessed by using complex computational models (e.g., expensive-to-run finite-element models). A direct use of Monte Carlo sim…
View article
Surrogate model of hybridized numerical relativity binary black hole waveforms Open
Numerical relativity (NR) simulations provide the most accurate binary black hole gravitational waveforms, but are prohibitively expensive for applications such as parameter estimation. Surrogate models of NR waveforms have been shown to b…
View article
The Remarkable Robustness of Surrogate Gradient Learning for Instilling Complex Function in Spiking Neural Networks Open
Brains process information in spiking neural networks. Their intricate connections shape the diverse functions these networks perform. Yet how network connectivity relates to function is poorly understood, and the functional capabilities o…
View article
Testing advanced driver assistance systems using multi-objective search and neural networks Open
Recent years have seen a proliferation of complex Advanced Driver Assistance Systems (ADAS), in particular, for use in autonomous cars. These systems consist of sensors and cameras as well as image processing and decision support software …
View article
Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models Open
Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, wh…
View article
Multi-physics-resolved digital twin of proton exchange membrane fuel cells with a data-driven surrogate model Open
The development of multi-physics-resolved digital twins of proton exchange membrane fuel cells (PEMFCs) is significant for the advancement of this technology. Here, to solve this scientific issue, a surrogate modelling method that combines…
View article
Low-Scaling Algorithm for Nudged Elastic Band Calculations Using a Surrogate Machine Learning Model Open
We present the incorporation of a surrogate Gaussian process regression (GPR) atomistic model to greatly accelerate the rate of convergence of classical nudged elastic band (NEB) calculations. In our surrogate model approach, the cost of c…
View article
Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization Open
The accelerated discovery of materials for real world applications requires the achievement of multiple design objectives. The multidimensional nature of the search necessitates exploration of multimillion compound libraries over which eve…
View article
Diesel Surrogate Fuels for Engine Testing and Chemical-Kinetic Modeling: Compositions and Properties Open
The primary objectives of this work were to formulate, blend, and characterize a set of four ultralow-sulfur diesel surrogate fuels in quantities sufficient to enable their study in single-cylinder-engine and combustion-vessel experiments.…
View article
Global and Local Surrogate-Assisted Differential Evolution for Expensive Constrained Optimization Problems With Inequality Constraints Open
For expensive constrained optimization problems (ECOPs), the computation of objective function and constraints is very time-consuming. This paper proposes a novel global and local surrogate-assisted differential evolution (DE) for solving …
View article
Surrogate‐based methods for black‐box optimization Open
In this paper, we survey methods that are currently used in black‐box optimization, that is, the kind of problems whose objective functions are very expensive to evaluate and no analytical or derivative information is available. We concent…
View article
Benchmarking the performance of Bayesian optimization across multiple experimental materials science domains Open
Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this wor…
View article
Data-driven topology optimization of spinodoid metamaterials with seamlessly tunable anisotropy Open
We present a two-scale topology optimization framework for the design of macroscopic bodies with an optimized elastic response, which is achieved by means of a spatially-variant cellular architecture on the microscale. The chosen spinodoid…
View article
Efficient Global Structure Optimization with a Machine-Learned Surrogate Model Open
We propose a scheme for global optimization with first-principles energy expressions of atomistic structure. While unfolding its search, the method actively learns a surrogate model of the potential energy landscape on which it performs a …
View article
Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids Open
A variable-fidelity method can remarkably improve the efficiency of a design optimization based on a high-fidelity and expensive numerical simulation, with assistance of lower-fidelity and cheaper simulation(s). However, most existing work…
View article
Surrogate Gradient Learning in Spiking Neural Networks Open
Spiking neural networks are nature's versatile solution to fault-tolerant and energy efficient signal processing. To translate these benefits into hardware, a growing number of neuromorphic spiking neural network processors attempt to emul…
View article
Multi objective optimization of computationally expensive multi-modal functions with RBF surrogates and multi-rule selection Open
GOMORS is a parallel response surface-assisted evolutionary algorithm approach to multi-objective optimization that is designed to obtain good non-dominated solutions to black box problems with relatively few objective function evaluations…
View article
A data-driven surrogate-assisted evolutionary algorithm applied to a many-objective blast furnace optimization problem Open
A new data-driven reference vector-guided evolutionary algorithm has been successfully implemented to construct surrogate models for various objectives pertinent to an industrial blast furnace. A total of eight objectives have been modeled…
View article
Probabilistic neural networks for fluid flow surrogate modeling and data recovery Open
We consider the use of probabilistic neural networks for fluid flow surrogate modeling and data recovery. This framework is constructed by assuming that the target variables are sampled from a Gaussian distribution conditioned on the input…
View article
Physics-informed neural networks as surrogate models of hydrodynamic simulators Open
In response to growing concerns surrounding the relationship between climate change and escalating flood risk, there is an increasing urgency to develop precise and rapid flood prediction models. Although high-resolution flood simulations …
View article
Efficient Hyperparameter Optimization for Deep Learning Algorithms Using Deterministic RBF Surrogates Open
Automatically searching for optimal hyperparameter configurations is of crucial importance for applying deep learning algorithms in practice. Recently, Bayesian optimization has been proposed for optimizing hyperparameters of various machi…
View article
Data-Driven Evolutionary Algorithm With Perturbation-Based Ensemble Surrogates Open
Data-driven evolutionary algorithms (DDEAs) aim to utilize data and surrogates to drive optimization, which is useful and efficient when the objective function of the optimization problem is expensive or difficult to access. However, the p…