Topological Bifurcations of Attracting 2-Tori of Quasiperiodically Driven Oscillators. - Brian K. Spears; Michael Hutchings; Andrew J. Szeri (J. Nonlinear Sci., 2005)

The Bold, New Extreme Programming Experiment. - Brian K. Spears (AGILE, 2009)

ND2AV: N-dimensional data analysis and visualization analysis for the National Ignition Campaign. - Peer-Timo Bremer; Dan Maljovec; Avishek Saha; Bei Wang 001; Jim Gaffney; Brian K. Spears; Valerio Pascucci (Comput. Vis. Sci., 2015)

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)

Contemporary machine learning: a guide for practitioners in the physical sciences. - Brian K. Spears (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)

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)

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion. - Rushil Anirudh; Jayaraman J. Thiagarajan; Shusen Liu 001; Peer-Timo Bremer; Brian K. Spears (CoRR, 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)

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)

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)

Improved surrogates in inertial confinement fusion with manifold and cycle consistencies. - Rushil Anirudh; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Brian K. Spears (Proc. Natl. Acad. Sci. USA, 2020)

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)

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)

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)

Geometric Priors for Scientific Generative Models in Inertial Confinement Fusion. - Ankita Shukla; Rushil Anirudh; Eugene Kur; Jayaraman J. Thiagarajan; Peer-Timo Bremer; Brian K. Spears; Tammy Ma; Pavan K. Turaga (CoRR, 2021)

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)

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)

AI-Augmented Facilities: Bridging Experiment and Simulation with ML (Dagstuhl Seminar 23132). - Peer-Timo Bremer; Brian K. Spears; Tom Gibbs; Michael Bussmann (Dagstuhl Reports, 2023)