Jim Gaffney

According to our database, Jim Gaffney authored at least 10 papers between 2015 and 2022.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2022

Suppressing simulation bias in multi-modal data using transfer learning.

Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan, Michael K. G. Kruse, Ryan Nora

Mach. Learn. Sci. Technol., 2022

Enabling machine learning-ready HPC ensembles with Merlin.

J. Luc Peterson, Benjamin Bay, Joe Koning, Peter B. Robinson, Jessica Semler, Jeremy White, Rushil Anirudh, Kevin Athey, Peer-Timo Bremer, Francesco Di Natale, David Fox, Jim A. Gaffney, Sam Ade Jacobs, Bhavya Kailkhura, Bogdan Kustowski, Steve H. Langer, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom

Future Gener. Comput. Syst., 2022

2021

The data-driven future of high-energy-density physics.

Peter W. Hatfield, Jim A. Gaffney, Gemma J. Anderson, Suzanne Ali, Luca Antonelli, Suzan Basegmez du Pree, Jonathan Citrin, Marta Fajardo, Patrick F. Knapp, Brendan Kettle, Bogdan Kustowski, Michael J. MacDonald, Derek Mariscal, Madison E. Martin, Taisuke Nagayama, Charlotte A. J. Palmer, J. Luc Peterson, Steven J. Rose, J. J. Ruby, Carl Shneider, Matt J. V. Streeter, Will Trickey, Ben Williams

Nat., 2021

Transfer learning suppresses simulation bias in predictive models built from sparse, multi-modal data.

Bogdan Kustowski, Jim A. Gaffney, Brian K. Spears, Gemma J. Anderson, Rushil Anirudh, Peer-Timo Bremer, Jayaraman J. Thiagarajan

CoRR, 2021

2020

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications.

Shusen Liu, Jim Gaffney, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom

IEEE Trans. Vis. Comput. Graph., 2020

Meaningful uncertainties from deep neural network surrogates of large-scale numerical simulations.

Gemma J. Anderson, Jim A. Gaffney, Brian K. Spears, Peer-Timo Bremer, Rushil Anirudh, Jayaraman J. Thiagarajan

CoRR, 2020

Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models.

Jayaraman J. Thiagarajan, Bindya Venkatesh, Rushil Anirudh, Peer-Timo Bremer, Jim Gaffney, Gemma Anderson, Brian K. Spears

CoRR, 2020

2019

Merlin: Enabling Machine Learning-Ready HPC Ensembles.

J. Luc Peterson, Rushil Anirudh, Kevin Athey, Benjamin Bay, Peer-Timo Bremer, Vic Castillo, Francesco Di Natale, David Fox, Jim A. Gaffney, David Hysom, Sam Ade Jacobs, Bhavya Kailkhura, Joe Koning, Bogdan Kustowski, Steven H. Langer, Peter B. Robinson, Jessica Semler, Brian K. Spears, Jayaraman J. Thiagarajan, Brian Van Essen, Jae-Seung Yeom

CoRR, 2019

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets.

Sam Ade Jacobs, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer

Proceedings of the 2019 IEEE International Conference on Cluster Computing, 2019

2015

ND²AV: N-dimensional data analysis and visualization analysis for the National Ignition Campaign.

Peer-Timo Bremer, Dan Maljovec, Avishek Saha, Bei Wang, Jim Gaffney, Brian K. Spears, Valerio Pascucci

Comput. Vis. Sci., 2015