5 papers
Importance of localized dilatation and distensibility in identifying determinants of thoracic aortic aneurysm with neural operators
David S. Li, Somdatta Goswami, Qianying Cao +4
Thoracic aortic aneurysms (TAAs) arise from diverse mechanical and mechanobiological disruptions to the aortic wall that increase the risk of dissection or rupture. Evidence links…
Learning Turbulent Flows with Generative Models: Super-resolution, Forecasting, and Sparse Flow Reconstruction
Vivek Oommen, Siavash Khodakarami, Aniruddha Bora +2
Neural operators are promising surrogates for dynamical systems but when trained with standard L2 losses they tend to oversmooth fine-scale turbulent structures. Here, we show that…
Equilibrium Conserving Neural Operators for Super-Resolution Learning
Vivek Oommen, Andreas E. Robertson, Daniel Diaz +5
Neural surrogate solvers can estimate solutions to partial differential equations in physical problems more efficiently than standard numerical methods, but require extensive high-…
Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems
Siavash Khodakarami, Vivek Oommen, Aniruddha Bora +1
Neural operators have emerged as powerful surrogates for modeling complex physical problems. However, they suffer from spectral bias making them oblivious to high-frequency modes,…
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
Jiawen Wei, Aniruddha Bora, Vivek Oommen +7
Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction ski…