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M. Giselle Fernández-Godino
M. Giselle Fernández-Godino
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Review of multi-fidelity models
MG Fernández-Godino
Advances in Computational Science and Engineering 1 (4), 351-400, 2023
329*2023
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
1722019
StressNet-Deep learning to predict stress with fracture propagation in brittle materials
Y Wang, D Oyen, WG Guo, A Mehta, CB Scott, N Panda, ...
npj Materials Degradation 5 (1), 1-10, 2021
352021
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
152019
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
132021
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
112019
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
92016
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
8*2021
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, 2021
72021
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
72021
On the use of symmetries in building surrogate models
M Giselle Fernández-Godino, S Balachandar, RT Haftka
Journal of Mechanical Design 141 (6), 2019
72019
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
52021
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
52018
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
52016
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
32023
Quantifying Particle Departure from Axisymmetry in Multiphase Cylindrical Detonation
MG Fernández-Godino
University of Florida, 2018
32018
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
22022
StressNet—Deep Learning to Predict Stress with Fracture Propagation in Brittle Materials. npj Mater
Y Wang, D Oyen, WG Guo, A Mehta, CB Scott, N Panda, ...
Degrad 5 (1), 1-10, 2021
2*2021
Review Of multi-fidelity surrogate models M
F Fernandez-Godino, C Park, NH Kim, RT Haftka
ECCOMAS Congress, 2016
22016
Review of multi-fidelity models. Fernández-Godino, M. Giselle, Chanyoung Park, Nam-Ho Kim, and Raphael T. Haftka." Review of multi-fidelity models."
G Fernandez
arXiv preprint arXiv:1609.07196 3, 2017, 2016
22016
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Artikelen 1–20