Articles with public access mandates - M. Giselle Fernández-GodinoLearn more
Not available anywhere: 1
Flyer Plate Continuum Simulations Informed with Machine Learning Crack Evolution
MG Fernandez-Godino, N Panda, D O'Malley, KS Hickmann, DA Oyen, ...
AIAA Scitech 2020 Forum, 1410, 2020
Mandates: US Department of Energy
Available somewhere: 17
Issues in deciding whether to use multifidelity surrogates
M Giselle Fernández-Godino, C Park, NH Kim, RT Haftka
Aiaa Journal 57 (5), 2039-2054, 2019
Mandates: US Department of Energy
StressNet-Deep learning to predict stress with fracture propagation in brittle materials
Y Wang, D Oyen, W Guo, A Mehta, CB Scott, N Panda, ...
Npj Materials Degradation 5 (1), 6, 2021
Mandates: US National Science Foundation, US Department of Energy
Linear regression based multi-fidelity surrogate for disturbance amplification in multi-phase explosion
MG Fernández-Godino, S Dubreuil, N Bartoli, C Gogu, S Balachandar, ...
Structural and Multidisciplinary Optimization 60 (6), 2205-22220, 2019
Mandates: US Department of Energy, Agence Nationale de la Recherche
Deep convolutional autoencoders as generic feature extractors in seismological applications
Q Kong, A Chiang, AC Aguiar, MG Fernández-Godino, SC Myers, ...
Artificial Intelligence in Geosciences 2, 96-106, 2021
Mandates: US Department of Energy
Early time evolution of circumferential perturbation of initial particle volume fraction in explosive cylindrical multiphase dispersion
MG Fernández-Godino, F Ouellet, RT Haftka, S Balachandar
Journal of Fluids Engineering 141 (9), 091302, 2019
Mandates: US Department of Energy
Anomaly Detection Using Groups of Simulations
MG Fernández–Godino, A Diggs, C Park, NH Kim, RT Haftka
18th AIAA Non-Deterministic Approaches Conference, 1195, 2016
Mandates: US Department of Energy
Identifying entangled physics relationships through sparse matrix decomposition to inform plasma fusion design
MG Fernández-Godino, MJ Grosskopf, JB Nakhleh, BM Wilson, JL Kline, ...
IEEE Transactions on Plasma Science 49 (8), 2410-2419, 2021
Mandates: US Department of Energy
Exploring sensitivity of icf outputs to design parameters in experiments using machine learning
JB Nakhleh, MG Fernández-Godino, MJ Grosskopf, BM Wilson, J Kline, ...
IEEE Transactions on Plasma Science 49 (7), 2238-2246, 2021
Mandates: US Department of Energy
Accelerating high-strain continuum-scale brittle fracture simulations with machine learning
MG Fernández-Godino, N Panda, D O’Malley, K Larkin, A Hunter, ...
Computational Materials Science 186, 109959, 2021
Mandates: US Department of Energy
Predicting wind‑driven spatial deposition through simulated color images using deep autoencoders
MG Fernández-Godino, DD Lucas, Q Kong
Nature Scientific Reports 13 (1394), 12, 2023
Mandates: US Department of Energy
On the use of symmetries in building surrogate models
M Giselle Fernández-Godino, S Balachandar, RT Haftka
Journal of Mechanical Design 141 (6), 2019
Mandates: US Department of Energy, Higher Education Authority, Ireland, Irish Research …
Noise filtering and uncertainty quantification in surrogate based optimization
MG Fernández-Godino, RT Haftka, S Balachandar, C Gogu, N Bartoli, ...
2018 AIAA Non-Deterministic Approaches Conference, 2176, 2018
Mandates: US Department of Energy
A data-driven non-linear assimilation framework with neural networks
N Panda, MG Fernández-Godino, HC Godinez, C Dawson
Computational Geosciences 25 (1), 233-242, 2021
Mandates: US Department of Energy
Validation, Uncertainty Quantification and Uncertainty Reduction for a Shock Tube Simulation
C Park, MG Fernández-Godino, NH Kim, RT Haftka
18th AIAA Non-Deterministic Approaches Conference, 1192, 2016
Mandates: US Department of Energy
Uncertainty bounds for multivariate machine learning predictions on high-strain brittle fracture
C Garcia-Cardona, MG Fernández-Godino, D O’Malley, T Bhattacharya
Computational Materials Science 201, 110883, 2022
Mandates: US Department of Energy, US National Institutes of Health
Analysis of Data Fusion Between Waveform Events and Radionuclide Detections Reported in 2021 by the International Data Centre
MG Fernández-Godino, DD Lucas, SC Myers
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2022
Mandates: US Department of Energy
Leveraging Local Scale Deep Autoencoder-based Models to Improve Early Time Predictions in Global Atmospheric Transport
MG Fernandez, WT Chung, DD Lucas
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States), 2023
Mandates: US National Science Foundation, US Department of Energy
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