Optimization methods employing approximation models originated in the 1970s and have proved extremely popular within the engineering community. In recent years, supporting mathematical theory has been developed to provide the foundation for a broad class of approximation-based optimization methods. Several surrogate model classes are available. First, the surrogate may be of data fitting type, involving a non-physics-based approximation involving interpolation or regression of quantities of interest generated from the original model. Alternatively, physics-based reduced-order modeling techniques such as Proper Orthogonal Decomposition (POD), Proper Generalized Decomposition (PGD), A Priori Hyper-Reduction, Reduced Basis promise better insight into full-field solutions. Multi-fidelity techniques (also called variable fidelity, variable complexity) combine different models within a model hierarchy. Simplified physics-based models (e.g., coarser mesh, reduced element order, relaxed solver tolerances, simplified physics) are then combined with the high-fidelity model to control the overall simulation budget.
This special issue aims to present recent innovative and powerful numerical approaches and applications in surrogate modeling for design optimization, including but not limited to multi-fidelity strategies and reduced-order models.
Particular topics of interest include but are not limited to:
- Surrogate modeling including Bayesian, RBF, infill criteria, gradient-enhanced, etc.
- Multi-fidelity, variable-complexity, variable-fidelity
- Reduced-order models
- Multiobjective optimization
- Connections with machine learning
- Quantum computing optimization
Lead Guest Editor:
Pierre-Alain Boucard, ENS Paris-Saclay, CNRS - LMT - Laboratoire de Mécanique et Technologie
Piotr Breitkopf, Sorbonne Université , UTC, CNRS – Laboratoire Roberval
Stefanie Reese, RWTH Aachen University - Institute of Apllied Mechanics
Pierre Duysinx, A&M - Automotive Engineering - University of Liege
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