4 citations · 12 across the 22 of their papers we have counts for
5 papers · 1 filter
Machine Learning-driven Multiscale MD Workflows: The Mini-MuMMI Experience
Loïc Pottier, Konstantia Georgouli, Timothy S. Carpenter +8
Computational models have become one of the prevalent methods to model complex phenomena. To accurately model complex interactions, such as detailed biomolecular interactions, scie…
HPAC-ML: A Programming Model for Embedding ML Surrogates in Scientific Applications
Zane Fink, Konstantinos Parasyris, Praneet Rathi +3
Recent advancements in Machine Learning (ML) have substantially improved its predictive and computational abilities, offering promising opportunities for surrogate modeling in scie…
Workflows Community Summit 2022: A Roadmap Revolution
Rafael Ferreira da Silva, Rosa M. Badia, Venkat Bala +102
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex s…
Scalable Comparative Visualization of Ensembles of Call Graphs
Suraj P. Kesavan, Harsh Bhatia, Abhinav Bhatele +3
Optimizing the performance of large-scale parallel codes is critical for efficient utilization of computing resources. Code developers often explore various execution parameters, s…
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Sam Ade Jacobs, Brian Van Essen, David Hysom +11
Training deep neural networks on large scientific data is a challenging task that requires enormous compute power, especially if no pre-trained models exist to initialize the proce…