36 citations · 69 across the 14 of their papers we have counts for
4 papers · 1 filter
From PINNs to PIKANs: Recent Advances in Physics-Informed Machine Learning
Juan Diego Toscano, Vivek Oommen, Alan John Varghese +4
Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and…
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence Modeling
Vivek Oommen, Aniruddha Bora, Zhen Zhang +1
We integrate neural operators with diffusion models to address the spectral limitations of neural operators in surrogate modeling of turbulent flows. While neural operators offer c…
Deep operator learning-based surrogate models for aerothermodynamic analysis of AEDC hypersonic waverider
Khemraj Shukla, Jasmine Ratchford, Luis Bravo +4
Neural networks are universal approximators that traditionally have been used to learn a map between function inputs and outputs. However, recent research has demonstrated that dee…
RiemannONets: Interpretable Neural Operators for Riemann Problems
Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap +1
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numeri…