Jayson Luc Peterson

According to our database, Jayson Luc Peterson authored at least 18 papers between 2013 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2023

A multifidelity Bayesian optimization method for inertial confinement fusion design.

Jingyi Wang, Nai Yuan Chiang, Andrew Gillette, J. Luc Peterson

CoRR, 2023

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

Transfer learning driven design optimization for inertial confinement fusion.

Kelli D. Humbird, Jayson Luc Peterson

CoRR, 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

Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data.

Kelli D. Humbird, J. Luc Peterson, J. Salmonson, Brian K. Spears

CoRR, 2021

Workflows Community Summit: Bringing the Scientific Workflows Community Together.

Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Dan Laney, Dong H. Ahn, Shantenu Jha, Carole A. Goble, Lavanya Ramakrishnan, J. Luc Peterson, Bjoern Enders, Douglas Thain, Ilkay Altintas, Yadu N. Babuji, Rosa M. Badia, Vivien Bonazzi, Tainã Coleman, Michael R. Crusoe, Ewa Deelman, Frank Di Natale, Paolo Di Tommaso, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Alex Ganose, Björn A. Grüning, Daniel S. Katz, Olga A. Kuchar, Ana Kupresanin, Bertram Ludäscher, Ketan Maheshwari, Marta Mattoso, Kshitij Mehta, Todd S. Munson, Jonathan Ozik, Tom Peterka, Loic Pottier, Tim Randles, Stian Soiland-Reyes, Benjamín Tovar, Matteo Turilli, Thomas D. Uram, Karan Vahi, Michael Wilde, Matthew Wolf, Justin M. Wozniak

CoRR, 2021

A Community Roadmap for Scientific Workflows Research and Development.

Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Ilkay Altintas, Rosa M. Badia, Bartosz Balis, Tainã Coleman, Frederik Coppens, Frank Di Natale, Bjoern Enders, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Daniel Garijo, Carole A. Goble, Dorran Howell, Shantenu Jha, Daniel S. Katz, Daniel E. Laney, Ulf Leser, Maciej Malawski, Kshitij Mehta, Loïc Pottier, Jonathan Ozik, J. Luc Peterson, Lavanya Ramakrishnan, Stian Soiland-Reyes, Douglas Thain, Matthew Wolf

Proceedings of the 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), 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

2019

Deep Neural Network Initialization With Decision Trees.

Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren

IEEE Trans. Neural Networks Learn. Syst., 2019

Parameter inference with deep jointly informed neural networks.

Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren

Stat. Anal. Data Min., 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

2018

Transfer learning to model inertial confinement fusion experiments.

Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren

CoRR, 2018

Predicting the time-evolution of multi-physics systems with sequence-to-sequence models.

Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren

CoRR, 2018

2017

Ensemble simulations of inertial confinement fusion implosions.

Ryan Nora, Jayson Luc Peterson, Brian K. Spears, John Everett Field, Scott Brandon

Stat. Anal. Data Min., 2017

Deep Jointly-Informed Neural Networks.

Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren

CoRR, 2017

2016

A HYDRA UQ Workflow for NIF Ignition Experiments.

Steven H. Langer, Brian K. Spears, J. Luc Peterson, John Everett Field, Ryan Nora, Scott Brandon

Proceedings of the Second Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization, 2016

2013

Positivity Preservation and Advection Algorithms with Applications to Edge Plasma Turbulence.

J. Luc Peterson, Gregory W. Hammett

SIAM J. Sci. Comput., 2013