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20232026
most citedPhysics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions

3 citations · 4 across the 5 of their papers we have counts for

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cs.LG2026

BladeSDF : Unconditional and Conditional Generative Modeling of Representative Blade Geometries Using Signed Distance Functions

Ashish S. Nair, Sandipp Krishnan Ravi, Itzel Salgado +3

Generative AI has emerged as a transformative paradigm in engineering design, enabling automated synthesis and reconstruction of complex 3D geometries while preserving feasibility…

cs.LG2025★ 1 cited

E-PINNs: Epistemic Physics-Informed Neural Networks

Bruno Jacob, Ashish S. Nair, Amanda A. Howard +2

Physics-informed neural networks (PINNs) have demonstrated promise as a framework for solving forward and inverse problems involving partial differential equations. Despite recent…