14 citations · 30 across the 9 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
IDLat: An Importance-Driven Latent Generation Method for Scientific Data
Jingyi Shen, Haoyu Li, Jiayi Xu +2
Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow f…
Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data
Subhashis Hazarika, Ayan Biswas, Phillip J. Wolfram +2
With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facil…
Deep Learning-Based Feature-Aware Data Modeling for Complex Physics Simulations
Qun Liu, Subhashis Hazarika, John M. Patchett +2
Data modeling and reduction for in situ is important. Feature-driven methods for in situ data analysis and reduction are a priority for future exascale machines as there are curren…