Scientific Machine Learning advances for fluid flow modeling detailed

This chapter reviews recent advancements in Scientific Machine Learning (SciML) for modeling complex fluid flow and transport phenomena. It surveys methods like Singular Value Decomposition, Dynamic Mode Decomposition, Physics-Informed Neural Networks (PINNs), and $\beta$-Variational Autoencoders ($\beta$-VAEs) for creating efficient surrogate models. The work combines these with High Performance Computing strategies and presents new contributions in modeling turbidity currents via PINNs and extracting disentangled nonlinear modes from thermal flows using $\beta$-VAEs. The chapter demonstrates how SciML can provide fast, accurate approximations of complex coupled systems at a substantially reduced computational cost. AI

IMPACT Enhances the efficiency and accuracy of complex fluid dynamics simulations through advanced machine learning techniques.

RANK_REASON The item is a review chapter detailing advancements in scientific machine learning for a specific research area, including methods and new contributions. [lever_c_demoted from research: ic=1 ai=1.0]

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• Adaptive Mesh Refinement/Coarsening
• $\beta$-Variational Autoencoders
• Dynamic Mode Decomposition
• High Performance Computing
• Physics-Informed Neural Networks
• Scientific Machine Learning
• Singular Value Decomposition

• paper
• infra

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