Positivity Preservation and Advection Algorithms with Applications to Edge Plasma Turbulence. - J. Luc Peterson; Gregory W. Hammett (SIAM J. Sci. Comput., 2013)

A HYDRA UQ Workflow for NIF Ignition Experiments. - Steven H. Langer; Brian K. Spears; J. Luc Peterson; John Everett Field; Ryan Nora; Scott Brandon (ISAV@SC, 2016)

Deep Jointly-Informed Neural Networks. - Kelli D. Humbird; J. Luc Peterson; Ryan G. McClarren (CoRR, 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)

Predicting the time-evolution of multi-physics systems with sequence-to-sequence models. - Kelli D. Humbird; J. Luc Peterson; Ryan G. McClarren (CoRR, 2018)

Transfer learning to model inertial confinement fusion experiments. - Kelli D. Humbird; J. Luc Peterson; Ryan G. McClarren (CoRR, 2018)

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; et al. (CLUSTER, 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; et al. (CoRR, 2019)

Parameter inference with deep jointly informed neural networks. - Kelli D. Humbird; J. Luc Peterson; Ryan G. McClarren (Stat. Anal. Data Min., 2019)

Deep Neural Network Initialization With Decision Trees. - Kelli D. Humbird; J. Luc Peterson; Ryan G. McClarren (IEEE Trans. Neural Networks Learn. Syst., 2019)

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications. - Shusen Liu 001; 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; et al. (IEEE Trans. Vis. Comput. Graph., 2020)

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; et al. (WORKS, 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; et al. (CoRR, 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)

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; et al. (Nat., 2021)

Transfer learning driven design optimization for inertial confinement fusion. - Kelli D. Humbird; Jayson Luc Peterson (CoRR, 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; et al. (Future Gener. Comput. Syst., 2022)

A multifidelity Bayesian optimization method for inertial confinement fusion design. - Jingyi Wang; Nai Yuan Chiang; Andrew Gillette; J. Luc Peterson (CoRR, 2023)

A terminology for scientific workflow systems. - Frédéric Suter; Tainã Coleman; Ilkay Altintas; Rosa M. Badia; Bartosz Balis; Kyle Chard; Iacopo Colonnelli; Ewa Deelman; Paolo Di Tommaso; Thomas Fahringer; Carole A. Goble; Shantenu Jha; et al. (Future Gener. Comput. Syst., 2026)