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ORCIDJayson Luc Peterson
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2020 – today
- 2023
[i11]Jingyi Wang, Nai Yuan Chiang, Andrew Gillette, J. Luc Peterson:
A multifidelity Bayesian optimization method for inertial confinement fusion design. CoRR abs/2312.10218 (2023)- 2022
- [j7]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:
Enabling machine learning-ready HPC ensembles with Merlin. Future Gener. Comput. Syst. 131: 255-268 (2022) - [i10]Kelli D. Humbird, Jayson Luc Peterson:
Transfer learning driven design optimization for inertial confinement fusion. CoRR abs/2205.13519 (2022) - 2021
- [j6]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:
The data-driven future of high-energy-density physics. Nat. 593(7859): 351-361 (2021) - [c3]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:
A Community Roadmap for Scientific Workflows Research and Development. WORKS 2021: 81-90 - [i9]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:
Workflows Community Summit: Bringing the Scientific Workflows Community Together. CoRR abs/2103.09181 (2021) - [i8]Kelli D. Humbird, J. Luc Peterson, J. Salmonson, Brian K. Spears:
Cognitive simulation models for inertial confinement fusion: Combining simulation and experimental data. CoRR abs/2103.10590 (2021) - [i7]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:
A Community Roadmap for Scientific Workflows Research and Development. CoRR abs/2110.02168 (2021) - 2020
- [j5]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:
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. IEEE Trans. Vis. Comput. Graph. 26(1): 291-300 (2020)
2010 – 2019
- 2019
- [j4]Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren:
Parameter inference with deep jointly informed neural networks. Stat. Anal. Data Min. 12(6): 496-504 (2019) - [j3]Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren:
Deep Neural Network Initialization With Decision Trees. IEEE Trans. Neural Networks Learn. Syst. 30(5): 1286-1295 (2019) - [c2]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:
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CLUSTER 2019: 1-10 - [i6]Shusen Liu, Di Wang, Dan Maljovec, Rushil Anirudh, Jayaraman J. Thiagarajan, Sam Ade Jacobs, Brian C. Van Essen, David Hysom, Jae-Seung Yeom, Jim Gaffney, J. Luc Peterson, Peter B. Robinson, Harsh Bhatia, Valerio Pascucci, Brian K. Spears, Peer-Timo Bremer:
Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. CoRR abs/1907.08325 (2019) - [i5]Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagarajan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter B. Robinson, J. Luc Peterson, Brian K. Spears:
Parallelizing Training of Deep Generative Models on Massive Scientific Datasets. CoRR abs/1910.02270 (2019) - [i4]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:
Merlin: Enabling Machine Learning-Ready HPC Ensembles. CoRR abs/1912.02892 (2019) - 2018
- [i3]Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren:
Predicting the time-evolution of multi-physics systems with sequence-to-sequence models. CoRR abs/1811.05852 (2018) - [i2]Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren:
Transfer learning to model inertial confinement fusion experiments. CoRR abs/1812.06055 (2018) - 2017
- [j2]Ryan Nora, Jayson Luc Peterson, Brian K. Spears, John Everett Field, Scott Brandon:
Ensemble simulations of inertial confinement fusion implosions. Stat. Anal. Data Min. 10(4): 230-237 (2017) - [i1]Kelli D. Humbird, J. Luc Peterson, Ryan G. McClarren:
Deep Jointly-Informed Neural Networks. CoRR abs/1707.00784 (2017) - 2016
- [c1]Steven H. Langer, Brian K. Spears, J. Luc Peterson, John Everett Field, Ryan Nora, Scott Brandon:
A HYDRA UQ Workflow for NIF Ignition Experiments. ISAV@SC 2016: 1-6 - 2013
- [j1]J. Luc Peterson, Gregory W. Hammett:
Positivity Preservation and Advection Algorithms with Applications to Edge Plasma Turbulence. SIAM J. Sci. Comput. 35(3) (2013)
Coauthor Index
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