5 citations · 12 across the 5 of their papers we have counts for
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PDEInvBench: A Comprehensive Dataset and Design Space Exploration of Neural Networks for PDE Inverse Problems
Divyam Goel, Nithin Chalapathi, Sanjeev Raja +1
Inverse problems in partial differential equations (PDEs) involve estimating the physical parameters of a system from observed spatiotemporal solution fields. Neural networks are w…
Scaling physics-informed hard constraints with mixture-of-experts
Nithin Chalapathi, Yiheng Du, Aditi Krishnapriyan
Imposing known physical constraints, such as conservation laws, during neural network training introduces an inductive bias that can improve accuracy, reliability, convergence, and…
Neural Spectral Methods: Self-supervised learning in the spectral domain
Yiheng Du, Nithin Chalapathi, Aditi Krishnapriyan
We present Neural Spectral Methods, a technique to solve parametric Partial Differential Equations (PDEs), grounded in classical spectral methods. Our method uses orthogonal bases…