28 citations · 71 across the 19 of their papers we have counts for
5 papers · 1 filter
A Stabilized Path-Space Approach to Diffusion-Based Posterior Sampling
Evan Scope Crafts, Umberto Villa, Saviz Mowlavi +3
Diffusion models provide expressive data-driven priors for Bayesian inverse problems, but many diffusion posterior samplers rely on heuristic guidance approximations that can fail…
OpInf-LLM: Parametric PDE Solving with LLMs via Operator Inference
Zhuoyuan Wang, Hanjiang Hu, Xiyu Deng +2
Solving diverse partial differential equations (PDEs) is fundamental in science and engineering. Large language models (LLMs) have demonstrated strong capabilities in code generati…
AB-PINNs: Adaptive-Basis Physics-Informed Neural Networks for Residual-Driven Domain Decomposition
Jonah Botvinick-Greenhouse, Wael H. Ali, Mouhacine Benosman +1
We introduce adaptive-basis physics-informed neural networks (AB-PINNs), a novel approach to domain decomposition for training PINNs in which existing subdomains dynamically adapt…
Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization
Xiaoyu Xie, Saviz Mowlavi, Mouhacine Benosman
Spatiotemporal modeling is critical for understanding complex systems across various scientific and engineering disciplines, but governing equations are often not fully known or co…
Reinforcement learning-based estimation for partial differential equations
Saviz Mowlavi, Mouhacine Benosman
In systems governed by nonlinear partial differential equations such as fluid flows, the design of state estimators such as Kalman filters relies on a reduced-order model (ROM) tha…