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Efficient strategies for surrogate-based optimization including multifidelity and reduced-order models

One of the main difficulties of optimization is the computational cost associated with the evaluation of numerical models. In order to overcome this problem, many works have emerged in the last few years based on model reduction methods, surrogate model and multifidelity and their coupling. This special issue focuses on these growing tools.

  1. Most of the methods used today for handling local stress constraints in topology optimization, fail to directly address the non-self-adjointness of the stress-constrained topology optimization problem. This in...

    Authors: Manyu Xiao, Jun Ma, Dongcheng Lu, Balaji Raghavan and Weihong Zhang
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:17
    Content type: Research article Published on:

  2. Most real optimization problems are defined over a mixed search space where the variables are both discrete and continuous. In engineering applications, the objective function is typically calculated with a nu...

    Authors: Jhouben Cuesta Ramirez, Rodolphe Le Riche, Olivier Roustant, Guillaume Perrin, Cédric Durantin and Alain Glière
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:6
    Content type: Research article Published on:

  3. Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors...

    Authors: Morgane Chapelier, Robin Bouclier and Jean-Charles Passieux
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:4
    Content type: Research article Published on:

  4. In design optimization of complex systems, the surrogate model approach relying on progressively enriched Design of Experiments (DOE) avoids efficiency problems encountered when embedding simulation codes with...

    Authors: Hanane Khatouri, Tariq Benamara, Piotr Breitkopf and Jean Demange
    Citation: Advanced Modeling and Simulation in Engineering Sciences 2022 9:1
    Content type: Review Published on: