Predictive modeling of a radiative shock system. - James Paul Holloway; Derek Bingham; Chuan-Chih Chou; Forrest Doss; R. Paul Drake; Bruce Fryxell; Michael Grosskopf; Bart van der Holst; Bani K. Mallick; Ryan G. McClarren; Ashin Mukherjee; Vijay N. Nair; et al. (Reliab. Eng. Syst. Saf., 2011)

A physics informed emulator for laser-driven radiating shock simulations. - Ryan G. McClarren; Duchwan Ryu; R. Paul Drake; Michael Grosskopf; Derek Bingham; Chuan-Chih Chou; Bruce Fryxell; Bart van der Holst; James Paul Holloway; Carolyn C. Kuranz; Bani K. Mallick; Erica Rutter; et al. (Reliab. Eng. Syst. Saf., 2011)

Prediction and Computer Model Calibration Using Outputs From Multifidelity Simulators. - Joslin Goh; Derek Bingham; James Paul Holloway; Michael Grosskopf; Carolyn C. Kuranz; Erica Rutter (Technometrics, 2013)

Emulation of Numerical Models With Over-Specified Basis Functions. - Avishek Chakraborty; Derek Bingham; Soma S. Dhavala; Carolyn C. Kuranz; R. Paul Drake; Michael J. Grosskopf; Erica M. Rutter; Ben R. Torralva; James Paul Holloway; Ryan G. McClarren; Bani K. Mallick (Technometrics, 2017)

Extreme Protection Against Data Loss with Single-Overlap Declustered Parity. - Huan Ke; Haryadi S. Gunawi; David Bonnie; Nathan DeBardeleben; Michael Grosskopf; Terry Grové; Dominic Manno; Elisabeth Moore; Brad Settlemyer (DSN, 2020)

Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning. - Julia B. Nakhleh; M. Giselle Fernández-Godino; Michael J. Grosskopf; Brandon M. Wilson; John Kline; Gowri Srinivasan (CoRR, 2020)

Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design. - M. Giselle Fernández-Godino; Michael J. Grosskopf; Julia B. Nakhleh; Brandon M. Wilson; John Kline; Gowri Srinivasan (CoRR, 2020)

Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. - Divya Banesh; Nishant Panda; Ayan Biswas; Luke Van Roekel; Diane Oyen; Nathan M. Urban; Michael Grosskopf; Jonathan David Wolfe; Earl Lawrence (IEEE BigData, 2021)

In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes. - Michael Grosskopf; Earl Lawrence; Ayan Biswas; Li Tang 007; Kelin Rumsey; Luke Van Roekel; Nathan M. Urban (ISAV@SC, 2021)

Generalized Computer Model Calibration for Radiation Transport Simulation. - Michael Grosskopf; Derek Bingham; Marvin L. Adams; W. Daryl Hawkins; Delia Perez-Nunez (Technometrics, 2021)

Fast emulation of density functional theory simulations using approximate Gaussian processes. - Steven Stetzler; Michael Grosskopf; Earl Lawrence (CoRR, 2022)

Temporal Characterization and Filtering of Sensor Data to Support Anomaly Detection. - Michael J. Grosskopf; Kary L. Myers; Earl Lawrence; Derek Bingham (Technometrics, 2022)

Driving mode analysis - How uncertain functional inputs propagate to an output. - Scott A. Vander Wiel; Michael J. Grosskopf; Isaac J. Michaud; Denise Neudecker (Stat. Anal. Data Min., 2024)

Expert-in-the-loop design of integral nuclear data experiments. - Isaac J. Michaud; Michael Grosskopf; Jesson Hutchinson; Scott A. Vander Wiel (Stat. Anal. Data Min., 2024)

Bayesian batch optimization for molybdenum versus tungsten inertial confinement fusion double shell target design. - Nomita N. Vazirani; Ryan Sacks; Brian M. Haines; Michael J. Grosskopf; David J. Stark; Paul A. Bradley (Stat. Anal. Data Min., 2024)

Machine learning-assisted identification of potential sources of bias in measurements of prompt-fission neutron spectra. - Noah A. W. Walton; Denise Neudecker; Scott A. Vander Wiel; Michael J. Grosskopf; Keegan J. Kelly (Comput. Phys. Commun., 2025)

Identifying Nuclear Data Correlated Through Predicting Bias in Integral Experiments via Applying Principal Component Analysis to Random Forest. - Brian Bell 002; Denise Neudecker; Michael Grosskopf; Jesson Hutchinson (Stat. Anal. Data Min., 2025)

URSA: The Universal Research and Scientific Agent. - Michael Grosskopf; Russell Bent; Rahul Somasundaram; Isaac J. Michaud; Arthur Lui; Nathan DeBardeleben; Earl Lawrence (CoRR, 2025)

A Partitioned Sparse Variational Gaussian Process for Fast, Distributed Spatial Modeling. - Michael Grosskopf; Kellin N. Rumsey; Ayan Biswas; Earl Lawrence (CoRR, 2025)