3 citations · 4 across the 5 of their papers we have counts for
5 papers
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…
Physics-Based Machine Learning Closures and Wall Models for Hypersonic Transition-Continuum Boundary Layer Predictions
Ashish S. Nair, Narendra Singh, Marco Panesi +2
Modeling rarefied hypersonic flows remains a fundamental challenge due to the breakdown of classical continuum assumptions in the transition-continuum regime, where the Knudsen num…
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…
Understanding Latent Timescales in Neural Ordinary Differential Equation Models for Advection-Dominated Dynamical Systems
Ashish S. Nair, Shivam Barwey, Pinaki Pal +3
The neural ordinary differential equation (ODE) framework has emerged as a powerful tool for developing accelerated surrogate models of complex physical systems governed by partial…
Deep Learning Closure of the Navier-Stokes Equations for Transition-Continuum Flows
Ashish S. Nair, Justin Sirignano, Marco Panesi +1
The predictive accuracy of the Navier-Stokes equations is known to degrade at the limits of the continuum assumption, thereby necessitating expensive and often highly approximate s…