A multi-objective Bayesian optimization environment for systematic design of numerical schemes for compressible flow

DOI10.1016/j.jcp.2022.111477OpenAlexW4286267541MaRDI QIDQ2168297

Felix S. Schranner, Yiqi Feng, J. M. Winter, Nikolaus A. Adams

Publication date: 31 August 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2022.111477

zbMATH Keywords

turbulent flowsexpected hypervolume improvementimplicit large eddy simulationmulti-objective Bayesian optimization

Mathematics Subject Classification ID

Mathematical programming (90Cxx) Basic methods in fluid mechanics (76Mxx) Fluid mechanics (76-XX)

Related Items (1)

A deep reinforcement learning framework for dynamic optimization of numerical schemes for compressible flow simulations

Uses Software

• GPyTorch
• DFVLR-SQP
• BoTorch

Cites Work

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