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Call for Papers: Efficient Strategies for Surrogate-Based Optimization Including Multifidelity and Reduced-Order Models

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

Guest Editors: 

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


Submission Instructions
Before submitting your manuscript, please ensure you have carefully read the submission guidelines for Advanced Modeling and Simulation in Engineering Sciences. Your complete manuscript should be submitted through the Advanced Modeling and Simulation in Engineering Sciences submission system, selecting inclusion with the thematic series, “Efficient Strategies for Surrogate-Based Optimization Including Multifidelity and Reduced-Order Models” when prompted. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.


Open Access Publication
Submissions will also benefit from the usual advantages of open access publication:Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient.
High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article.
No space constraints: Publishing online means unlimited space for figures, extensive data and video footage.
Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed.

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