4 papers · 1 filter
LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks
Nilay Anurag, Shital Adhikari, Taniya Kapoor +1
Physics-informed neural networks (PINNs) have had a broad research impact in modeling domains governed by partial differential equations (PDE). However, PINNs have been shown to pe…
TRIE: An Evaluation Framework for Stochastic PDE Surrogates
Bharat Srikishan, Javier E. Santos, Nikhil Muralidhar +1
Many scientific systems exhibit uncertainty from stochastic forcing, unresolved degrees of freedom, or imperfect observations, making reliable surrogate forecasting fundamentally d…
Model-Agnostic Knowledge Guided Correction for Improved Neural Surrogate Rollout
Bharat Srikishan, Daniel O'Malley, Mohamed Mehana +2
Modeling the evolution of physical systems is critical to many applications in science and engineering. As the evolution of these systems is governed by partial differential equati…
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation
Bharat Srikishan, Anika Tabassum, Srikanth Allu +2
Deep learning architectures have achieved state-of-the-art (SOTA) performance on computer vision tasks such as object detection and image segmentation. This may be attributed to th…