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Surrogate Modeling
Surrogate models are mathematical models widely used to replace physical experiments or complicated numerical simulations in various engineering...
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Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models
Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional...
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Connecting Agent-Based Models with High-Dimensional Parameter Spaces to Multidimensional Data Using SMoRe ParS: A Surrogate Modeling Approach
Across a broad range of disciplines, agent-based models (ABMs) are increasingly utilized for replicating, predicting, and understanding complex...
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Comparing Surrogate Models for Tuning Optimization Algorithms
Tuning an algorithm requires to evaluate it under different configurations on several problem instances. Such evaluations are costly. A way to reduce...
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Optimization of Artificial Viscosity in Production Codes Based on Gaussian Regression Surrogate Models
To accurately model flows with shock waves using staggered-grid Lagrangian hydrodynamics, the artificial viscosity has to be introduced to convert...
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Multi-fidelity surrogate-based optimal design of road vehicle suspension systems
Ride comfort is a relevant performance for road vehicles. The suspension system can filter vibration caused by the uneven road to improve ride...
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Surrogate based optimization approach for the calibration of cavitation models
The accuracy of CFD simulations is heavily influenced by the empirical coefficients employed by the mathematical models adopted to describe different...
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Surrogate Membership for Inferred Metrics in Fairness Evaluation
As artificial intelligence becomes more embedded into daily activities, it is imperative to ensure models perform well for all subgroups. This is...
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A surrogate-assisted a priori multiobjective evolutionary algorithm for constrained multiobjective optimization problems
We consider multiobjective optimization problems with at least one computationally expensive constraint function and propose a novel...
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Surrogate-based branch-and-bound algorithms for simulation-based black-box optimization
Black-box surrogate-based optimization has received increasing attention due to the growing interest in solving optimization problems with embedded...
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A surrogate-assisted evolutionary algorithm with clustering-based sampling for high-dimensional expensive blackbox optimization
Many practical problems involve the optimization of computationally expensive blackbox functions. The computational cost resulting from expensive...
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Physics-Based Active Learning for Design Space Exploration and Surrogate Construction for Multiparametric Optimization
The sampling of the training data is a bottleneck in the development of artificial intelligence (AI) models due to the processing of huge amounts of...
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Linewalker: line search for black box derivative-free optimization and surrogate model construction
This paper describes a simple, but effective sampling method for optimizing and learning a discrete approximation (or surrogate) of a...
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Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery
In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we...
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A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate
Understanding the dynamics of phase boundaries in fluids requires quantitative knowledge about the microscale processes at the interface. We consider...
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An improved interval model updating method via adaptive Kriging models
Interval model updating (IMU) methods have been widely used in uncertain model updating due to their low requirements for sample data. However, the...
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Surrogate-Based Reduced-Dimension Global Optimization in Process Systems Engineering
High dimensional global optimization problems arise frequently in process systems engineering. This is a result of the complex mechanistic...
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Multi-objective Parameter Tuning with Dynamic Compositional Surrogate Models
Multi-objective parameter tuning is a highly-practical black-box optimization problem, in which the target system is expensive to evaluate. To...
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Aerodynamic shape optimization of porous fences with curved deflectors using surrogate modelling
Wind fences are commonly used to mitigate dust emission from coal stockpiles in an open storage yard. There has substantial progress in the...
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Combining Stochastic Models with Machine Learning
Machine learning has become a prevalent and powerful tool in many scientific and engineering disciplines. This last chapter presents a few methods...