Scientific Machine Learning advances fluid dynamics modeling · 2 sources tracked

This chapter explores advancements in Scientific Machine Learning (SciML) for simulating complex fluid flow and transport phenomena. It details methods like Singular Value Decomposition, Dynamic Mode Decomposition, Physics-Informed Neural Networks (PINNs), and $\beta$-Variational Autoencoders ($\beta$-VAEs) to create efficient surrogate models. The work combines these techniques with High Performance Computing strategies, including Adaptive Mesh Refinement/Coarsening (AMR/C), to reduce computational costs for applications such as turbidity currents and thermal convection. AI

IMPACT Enables faster, more accurate approximations of complex fluid systems, reducing computational costs for scientific simulations.

RANK_REASON The cluster discusses a scientific paper detailing advancements in machine learning for fluid dynamics simulations.

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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
• Hugging Face
• Navier-Stokes Equations
• Variational Autoencoders

• paper
• infra

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