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Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods [PDF]
Data Science and Engineering, 2022 Surrogate modeling has been popularized as an alternative to full-scale models in complex engineering processes such as manufacturing and computer-assisted engineering.
Chun Kit Jeffery Hou, Kamran Behdinan
exaly +3 more sources
A Python surrogate modeling framework with derivatives [PDF]
Advances in Engineering Software, 2019 The surrogate modeling toolbox (SMT) is an open-source Python package that contains a collection of surrogate modeling methods, sampling techniques, and benchmarking functions. This package provides a library of surrogate models that is simple to use and
Mohamed Amine Bouhlel +2 more
exaly +6 more sources
Scalable Transformer for PDE Surrogate Modeling [PDF]
Advances in Neural Information Processing Systems 36, 2023 Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PDEs).
Zijie Li, Dule Shu, A. Farimani
semanticscholar +4 more sources
Recent Advances in Surrogate Modeling Methods for Uncertainty Quantification and Propagation
Symmetry, 2022 Surrogate-model-assisted uncertainty treatment practices have been the subject of increasing attention and investigations in recent decades for many symmetrical engineering systems.
Chong Wang, Xin Qiang, Menghui Xu
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Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data [PDF]
Journal of Computational Physics, 2019 Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for training.
Yinhao Zhu +2 more
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Higher-order factorization machine for accurate surrogate modeling in material design [PDF]
Scientific ReportsEfficient and robust optimization is important in material science for identifying optimal structural parameters and enhancing material performance. Surrogate-based active learning algorithms have recently gained great attention for their ability to ...
Sanghyo Hwang +4 more
doaj +2 more sources
Chemical Engineering Research and Design, 2021 Surrogate models are used to map input data to output data when the actual relationship between the two is unknown or computationally expensive to evaluate for several applications, including surface approximation and surrogate-based optimization.
Bianca Williams, Selen Cremaschi
exaly +2 more sources
This repository contains attachments related to the publication:**[Machine Learning Surrogate Optimization Framework for an Additively Manufactured, Linearly Polarized Metal Coaxial-to-Circular Waveguide Transition for Space Applications] ****Published in: ** [Engineering Applications of AI], [Volume xxx], [Issue yyy], [Year 2025], [Page Numbers zzz or
XYZ, ABC
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Data-driven surrogate modeling of high-resolution sea-ice thickness in the Arctic [PDF]
The CryosphereA novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km.
C. Durand +5 more
doaj +2 more sources
Surrogate-Based EM Design of RF and Microwave Components: A Systematic Review of Workflow Roles, Inverse Design, Multifidelity, and Active Learning [PDF]
SensorsSurrogate models have been increasingly used to reduce the computational cost of electromagnetic (EM) design in RF and microwave components. However, component types, surrogate model families, and design workflows vary substantially across the literature.
Maria Prousali, Stelios Tsitsos
doaj +2 more sources