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A machine learning surrogate modeling benchmark for temperature field reconstruction of heat source systems
The temperature field reconstruction of heat source systems (TFR-HSS) with limited monitoring sensors in thermal management plays an important role...
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Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods
Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and...
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Surrogate modeling for interactive tunnel track design using the cut finite element method
The Cut Finite Element Method (CutFEM) is recently shown to be a versatile approach for tunnel construction modeling and settlement analysis. The...
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A novel multi-fidelity surrogate modeling method for non-hierarchical data fusion
Multi-fidelity (MF) surrogate model has been widely used in simulation-based engineering design processes to reduce the computational cost, with a...
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Surrogate Modeling of Agent-Based Airport Terminal Operations
The airport terminals are complex sociotechnical systems, which are difficult to understand and their behavior is hard to predict. Hence, an...
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Surrogate Modeling for Stochastic Assessment of Engineering Structures
In many engineering problems, the response function such as the strain or stress field of the structure, its load-bearing capacity, deflection, etc.,...
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Physics-Informed Neural Network Surrogate Modeling Approach of Active/Passive Flow Control for Drag Reduction
This paper presents a novel surrogate modeling method with physics constraints that is specifically designed for optimal design in flow control...
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Optimisation of a Chemical Process by Using Machine Learning Algorithms with Surrogate Modeling
Process models are getting more detailed, thus computational costs are rising. For this reason, the main aim of process engineering is to provide...
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Surrogate modeling for spacecraft thermophysical models using deep learning
Thermal modeling is a critical technology in spacecraft thermal control systems, where the complex spatially and temporally variable parameters used...
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Towards a Better Understanding of Agent-Based Airport Terminal Operations Using Surrogate Modeling
Airport terminals are complex sociotechnical systems, in which humans interact with diverse technical systems. A natural way to represent them is...
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Global Surrogate Modeling by Neural Network-Based Model Uncertainty
This work proposes a novel adaptive global surrogate modeling algorithm which uses two neural networks, one for prediction and the other for the...
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Surrogate Models for the Compressibility Factor of Natural Gas
The paper presents an example of the so-called surrogate modeling. This is a computer modeling technique where machine learning methods are used to...
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A sparse multi-fidelity surrogate-based optimization method with computational awareness
CoKriging is a popular surrogate modeling approach to approximate the input–output relationship using multi-fidelity data from different sources....
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Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling
In the framework of reduced basis methods, we recently introduced a new certified hierarchical and adaptive surrogate model, which can be used for...
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Enhancing surrogate-assisted evolutionary optimization for medium-scale expensive problems: a two-stage approach with unsupervised feature learning and Q-learning
This paper presents a novel two-stage progressive search approach with unsupervised feature learning and Q-learning (TSLL) to enhance...
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Can (and Should) Automated Surrogate Modelling Be Used for Simulation Assistance?
Recent advances in machine learning may be leveraged by researchers in the context of agent-based modelling. With the help of surrogate models,...
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Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation
Surrogate models play a crucial role in retrospectively interpreting complex and powerful black box machine learning models via model distillation....
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Surrogate-assisted evolutionary sampling particle swarm optimization for high-dimensional expensive optimization
Surrogate-assisted evolutionary algorithms have been widely employed for solving expensive optimization problems. To address high-dimensional...
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Transformer Surrogate Genetic Programming for Dynamic Container Port Truck Dispatching
In the wake of burgeoning demands on port logistics, optimizing the operational efficiency of container ports has become a compelling necessity. A...
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Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression
Surrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built...