Direct shape optimization through deep reinforcement learning
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Publication:2128361
DOI10.1016/j.jcp.2020.110080OpenAlexW3113558818MaRDI QIDQ2128361
Alexander Kuhnle, Elie Hachem, Jonathan Viquerat, Aurélien Larcher, Hassan Ghraieb, Jean Rabault
Publication date: 21 April 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1908.09885
Related Items (7)
A multi-objective Bayesian optimization environment for systematic design of numerical schemes for compressible flow ⋮ Multi-condition multi-objective optimization using deep reinforcement learning ⋮ A deep reinforcement learning framework for dynamic optimization of numerical schemes for compressible flow simulations ⋮ Non-iterative generation of an optimal mesh for a blade passage using deep reinforcement learning ⋮ Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology ⋮ Optimised graded metamaterials for mechanical energy confinement and amplification via reinforcement learning ⋮ Multi-fidelity reinforcement learning framework for shape optimization
Uses Software
Cites Work
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