Ricardo Vinuesa, Steve Brunton
Published 2022-11-28Version 1
The renewed interest from the scientific community in machine learning (ML) is opening many new areas of research. Here we focus on how novel trends in ML are providing opportunities to improve the field of computational fluid dynamics (CFD). In particular, we discuss synergies between ML and CFD that have already shown benefits, and we also assess areas that are under development and may produce important benefits in the coming years. We believe that it is also important to emphasize a balanced perspective of cautious optimism for these emerging approaches
Computational Fluid Dynamics and Machine Learning as tools for Optimization of Micromixers geometry
Uncertainty Quantification in Computational Fluid Dynamics: Physics and Machine Learning Based Approaches
Implementation of the Critical Wave Groups Method with Computational Fluid Dynamics and Neural Networks
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