1 citations · 1 across the 3 of their papers we have counts for
4 papers
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…
Multigroup Thermal Radiation Transport with Tensor Trains
Aditya S. Deshpande, Patrick D. Mullen, Alex A. Gorodetsky +4
We investigate the application of tensor-train (TT) algorithms to multigroup thermal radiation transport (i.e., photon radiation transport). The TT framework enables simulations at…
Characterizing Adaptive Mesh Refinement on Heterogeneous Platforms with Parthenon-VIBE
Akash Poptani, Alireza Khadem, Scott Mahlke +3
Hero-class HPC simulations rely on Adaptive Mesh Refinement (AMR) to reduce compute and memory demands while maintaining accuracy. This work analyzes the performance of Parthenon,…
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…