Papers
1–4 of 4Two Teachers Better Than One: Hardware-Physics Co-Guided Distributed Scientific Machine Learning
Scientific machine learning (SciML) is increasingly applied to in-field processing, controlling, and monitoring; however, wide-area sensing, real-time demands, and strict energy and reliability constr...
ArGEnT: Arbitrary Geometry-encoded Transformer for Operator Learning
Learning solution operators for systems with complex, varying geometries and parametric physical settings is a central challenge in scientific machine learning. In many-query regimes such as design op...
Physics-informed fine-tuning of foundation models for partial differential equations
Foundation models for partial differential equations (PDEs) have emerged as powerful surrogates pre-trained on diverse physical systems, but adapting them to new downstream tasks remains challenging d...
Learning Where the Physics Is: Probabilistic Adaptive Sampling for Stiff PDEs
Modeling stiff partial differential equations (PDEs) with sharp gradients remains a significant challenge for scientific machine learning. While Physics-Informed Neural Networks (PINNs) struggle with ...