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)

StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials. - Yinan Wang; Diane Oyen; Weihong Guo 001; Anishi Mehta; Cory Braker Scott; Nishant Panda; M. Giselle Fernández-Godino; Gowri Srinivasan; Xiaowei Yue (CoRR, 2020)

Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture. - Cristina Garcia-Cardona; M. Giselle Fernández-Godino; Daniel O'Malley; Tanmoy Bhattacharya 001 (CoRR, 2020)

Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications. - Qingkai Kong; Andrea Chiang; Ana C. Aguiar; M. Giselle Fernández-Godino; Stephen C. Myers; Donald D. Lucas (CoRR, 2021)

Learning Physics through Images: An Application to Wind-Driven Spatial Patterns. - M. Giselle Fernández-Godino; Donald D. Lucas; Qingkai Kong (CoRR, 2022)

Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning. - Yinan Wang; M. Giselle Fernández-Godino; Nipun Gunawardena; Donald D. Lucas; Xiaowei Yue (CoRR, 2024)

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport. - M. Giselle Fernández-Godino; Wai Tong Chung; Akshay A. Gowardhan; Matthias Ihme; Qingkai Kong; Donald D. Lucas; Stephen C. Myers (CoRR, 2024)