SciML

Predicting electronic structures at any length scale with machine learning
Lenz Fiedler, Normand Modine, Steve Scmerler, Dayton J Vogel, Gabriel Popoola, Aidan P Thompson, Sivasankaran Rajamanickam, Attila Cangi
A Comparison of Spectral and Spatial Graph Convolutional Neural Network Kernels Using GraphSAGE-Sparse
Michael Eydenberg, Mark Plagge, Sivasankaran Rajamanickam
Scientific Machine Learning and Data flow acceleration: ASCR HQ Update
Mar 1, 2023 12:00 AM Albuquerque
Training-free hyperparameter optimization of neural networks for electronic structures in matter
Lenz Fiedler, Nils Hoffmann, Parvez Mohammed, Gabriel A. Popoola, Tamar Yovell, Vladyslav Oles, J. Austin Ellis, Sivasankaran Rajamanickam, Attila Cangi
Computational Challenges in the development of a surrogate model for Density Functional Theory
Sep 14, 2022 12:00 AM — 12:00 AM Gorlitz, Germany
Concentric Spherical Neural Network for 3D Representation Learning
James Fox, Bo Zhao, Beatriz Gonzalez del Rio, Sivasankaran Rajamanickam, Rampi Ramprasad, Le Song
Machine Learning for Materials Modeling
Jun 6, 2022 12:00 AM Albuquerque
Machine Learning for Science
Machine learning has resulted in several advances for several industry use cases and offers potential to accelerate science simulations and new scientific discoveries. My specific interests lie in developing machine learning based surrogate models for scientific simulations, especially on problems that require very large amount of data and/or compute.
Mala
See Mala Website for more details.
Jan 8, 2022
Enabling Science Simulations with Scalable Computational Frameworks for Scientific Computing
Oct 19, 2021 10:00 AM — 11:00 AM Google X.
Using Machine Learning to Accelerate Science Calculations
Oct 6, 2021 11:00 AM — 12:00 PM Sandia National Laboratories, CA.
Opportunities in Co-design: Exemplars from use of AI/ML in physical science, data science on AI/ML accelerators
Apr 7, 2021 11:15 AM — 12:00 PM Sandia National Laboratories, NM.
Accelerating finite-temperature Kohn-Sham density functional theory with deep neural networks
J Austin Ellis, Lenz Fiedler, Gabriel A Popoola, Normand A Modine, J Adam Stephens, Aidan P Thompson, Attila Cangi, Sivasankaran Rajamanickam
Co-design center for exascale machine learning technologies (ExaLearn)
Francis J Alexander, James Ang, Jenna A Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, Sutanay Choudhury, Bert Debusschere, Anthony M DeGennaro, others
Concentric Spherical GNN for 3D Representation Learning
James Fox, Bo Zhao, Sivasankaran Rajamanickam, Rampi Ramprasad, Le Song
Union: A unified HW-SW Co-Design ecosystem in MLIR for evaluating tensor operations on spatial accelerators
Geonhwa Jeong, Gokcen Kestor, Prasanth Chatarasi, Angshuman Parashar, Po-An Tsai, Sivasankaran Rajamanickam, Roberto Gioiosa, Tushar Krishna
Towards simulations on the Exascale hardware and beyond
Dec 3, 2020 3:30 PM — 5:00 PM University of Texas, Austin.
Intersection of Machine Learning and Scientific Simulations: Architectures and Applications perspectives
Oct 6, 2020 9:00 AM — 10:00 AM Online.
Recent Experiences with Machine Learning: Perspectives from Algorithms, Architectures and Applications
Aug 3, 2020 1:10 PM — 1:50 PM Online.
Scalable inference for sparse deep neural networks using Kokkos kernels
J Austin Ellis, Sivasankaran Rajamanickam