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cs.LG2026
Backward-Consistent Diffusion Sampling for Sparsely Observed PDE Inverse Problems
Yida Pan, Muhammad H. Ashiq, Chanyong Jung +4
Recovering Partial Differential Equation (PDE) coefficient fields from extremely sparse observations is a severely ill-posed inverse problem for which generative machine learning m…
cs.LG2024★ 1 cited
What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning
Arvind Mohan, Ashesh Chattopadhyay, Jonah Miller
Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often cal…