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

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)

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)

Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements. - Michael D. Vander Wal; Ryan G. McClarren; Kelli D. Humbird (CoRR, 2021)

Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models. - Michael D. Vander Wal; Ryan G. McClarren; Kelli D. Humbird (CoRR, 2022)

Transfer learning driven design optimization for inertial confinement fusion. - Kelli D. Humbird; Jayson Luc Peterson (CoRR, 2022)

Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations. - Michael D. Vander Wal; Ryan G. McClarren; Kelli D. Humbird (CoRR, 2022)