1 citations · 1 across the 7 of their papers we have counts for
7 papers
High-Fidelity Industrial Crash Dynamics Prediction via Geometry-Aware Operator Learning with Memory-Efficient Low-Rank Attention
Deepak Akhare, Mohammad Amin Nabian, Corey Adams +2
Automotive crashworthiness optimization remains a safety-critical challenge, requiring the management of large-scale nonlinear structural deformations and energy dissipation throug…
HiLiftAeroML: A High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics
Neil Ashton, Adam Clark, Liam Heidt +13
HiLiftAeroML is, to our knowledge, the first open high-fidelity computational fluid dynamics dataset dedicated to high-lift aircraft aerodynamics. It contains 1,800 simulations spa…
ShardTensor: Domain Parallelism for Scientific Machine Learning
Corey Adams, Peter Harrington, Akshay Subramaniam +4
Scientific Machine Learning (SciML) faces unique challenges for extreme-resolution data, with mitigations that often fail to scale or degrade the accuracy of trained models. While…
Learning Accurate Storm-Scale Evolution from Observations
Jaideep Pathak, Mohammad Shoaib Abbas, Peter Harrington +10
Accurate short-term prediction of clouds and precipitation is critical for severe weather warnings, aviation safety, and renewable energy operations. Forecasts at this timescale ar…
GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer
Corey Adams, Rishikesh Ranade, Ram Cherukuri +1
We present GeoTransolver, a multiscale geometry-aware physics attention transformer for Computer Aided Engineering (CAE). GeoTransolver extends the Transolver backbone with GALE (G…
An Efficient, Scalable IO Framework for Sparse Data: larcv3
Corey Adams, Kazuhiro Terao, Marco Del Tutto +1
Neutrino physics is one of the fundamental areas of research into the origins and properties of the Universe. Many experimental neutrino projects use sophisticated detectors to obs…
Scaling Distributed Training of Flood-Filling Networks on HPC Infrastructure for Brain Mapping
Wushi Dong, Murat Keceli, Rafael Vescovi +9
Mapping all the neurons in the brain requires automatic reconstruction of entire cells from volume electron microscopy data. The flood-filling network (FFN) architecture has demons…