18 citations · 47 across the 18 of their papers we have counts for
4 papers · 1 filter
Operator Learning Using Weak Supervision from Walk-on-Spheres
Hrishikesh Viswanath, Hong Chul Nam, Xi Deng +3
Training neural PDE solvers is often bottlenecked by expensive data generation or unstable physics-informed neural network (PINN) involving challenging optimization landscapes due…
Self-Supervised Learning via Flow-Guided Neural Operator on Time-Series Data
Duy Nguyen, Jiachen Yao, Jiayun Wang +2
Self-supervised learning (SSL) is a powerful paradigm for learning from unlabeled time-series data. However, popular methods such as masked autoencoders (MAEs) rely on reconstructi…
Decoupled Diffusion Sampling for Inverse Problems on Function Spaces
Thomas Y. L. Lin, Jiachen Yao, Lufang Chiang +2
We propose a data-efficient, physics-aware generative framework in function space for inverse PDE problems. Existing plug-and-play diffusion posterior samplers represent physics im…
Bridge Matching Sampler: Scalable Sampling via Generalized Fixed-Point Diffusion Matching
Denis Blessing, Lorenz Richter, Julius Berner +2
Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squares `matching' objectives have i…