M. Giselle Fernández-Godino

According to our database, M. Giselle Fernández-Godino authored at least 11 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Multi-Agent Collaboration for Automated Design Exploration on High Performance Computing Systems.

Harshitha Menon, Charles F. Jekel, Kevin Korner, Brian Gunnarson, Nathan K. Brown, Michael Stees, M. Giselle Fernández-Godino, Walter Nissen, Meir H. Shachar, Dane M. Sterbentz, William Schill, Yue Hao, Robert N. Rieben, William Roshan Quadros, Steven Owen, Scott Mitchell, Ismael D. Boureima, Jonathan L. Belof

CoRR, March, 2026

2025

Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy.

Meir H. Shachar, Dane M. Sterbentz, Harshitha Menon, Charles F. Jekel, M. Giselle Fernández-Godino, Nathan K. Brown, Ismael D. Boureima, Yue Hao, Kevin Körner, Robert N. Rieben, Daniel A. White, William Schill, Jonathan L. Belof

CoRR, October, 2025

Spatio-temporal, multi-field deep learning of shock propagation in meso-structured media.

M. Giselle Fernández-Godino, Meir H. Shachar, Kevin Körner, Jonathan L. Belof, Mukul Kumar, Jonathan Lind, William Schill

CoRR, September, 2025

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

Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning.

Yinan Wang, M. Giselle Fernández-Godino, Nipun Gunawardena, Donald D. Lucas, Xiaowei Yue

CoRR, 2024

2022

Learning Physics through Images: An Application to Wind-Driven Spatial Patterns.

M. Giselle Fernández-Godino, Donald D. Lucas, Qingkai Kong

CoRR, 2022

2021

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

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

CoRR, 2020

StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials.

Yinan Wang, Diane Oyen, Weihong Guo, Anishi Mehta, Cory Braker Scott, Nishant Panda, M. Giselle Fernández-Godino, Gowri Srinivasan, Xiaowei Yue

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

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