5 papers
Do Physics Foundation Models Learn Generalizable Physics? A Bias-Aware Benchmark Across Physical Regimes and Distribution Shifts
Mengdi Chu, Yang Liu, Ayan Biswas +1
Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single average score under a fixed traini…
DiffUNet^2: Bidirectional Prediction, Probabilistic Generation and Collaborative Visual Discovery for Scientific Data
Mengdi Chu, Jiaxin Yang, Angus G. Forbes +4
Modeling temporal evolution is important to analyzing and reasoning about scientific phenomena, yet most machine learning methods provide deterministic forward predictions that ove…
PDE foundation model-accelerated inverse estimation of system parameters in inertial confinement fusion
Mahindra Rautela, Alexander Scheinker, Bradley Love +4
PDE foundation models are typically pretrained on large, diverse corpora of PDE datasets and can be adapted to new settings with limited task-specific data. However, most downstrea…
Efficient Level-Crossing Probability Calculation for Gaussian Process Modeled Data
Haoyu Li, Isaac J Michaud, Ayan Biswas +1
Almost all scientific data have uncertainties originating from different sources. Gaussian process regression (GPR) models are a natural way to model data with Gaussian-distributed…
A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling
Michael Grosskopf, Kellin Rumsey, Ayan Biswas +1
The next generation of Department of Energy supercomputers will be capable of exascale computation. For these machines, far more computation will be possible than that which can be…