3 citations · 4 across the 11 of their papers we have counts for
4 papers · 1 filter
Sim+Real: Joint Simulation-Experiment Training Improves Balanced Prediction in Physical Systems
Mahindra Rautela, Alexander Scheinker, Ayan Biswas +3
Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment fine-tuning optimizes only t…
Out-of-distribution transfer of PDE foundation models to material dynamics under extreme loading
Mahindra Rautela, Alexander Most, Siddharth Mansingh +9
Most PDE foundation models are pretrained and fine-tuned on fluid-centric benchmarks. Their utility under extreme-loading material dynamics remains unclear. We benchmark out-of-dis…
Towards Reasoning for PDE Foundation Models: A Reward-Model-Driven Inference-Time-Scaling Algorithm
Siddharth Mansingh, James Amarel, Ragib Arnab +10
Partial Differential Equations (PDEs) are the bedrock for modern computational sciences and engineering, and inherently computationally expensive. While PDE foundation models have…
Explainable AI Integrated Feature Engineering for Wildfire Prediction
Di Fan, Ayan Biswas, James Paul Ahrens
Wildfires present intricate challenges for prediction, necessitating the use of sophisticated machine learning techniques for effective modeling\cite{jain2020review}. In our resear…