M. Giselle Fernández-Godino

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Raphael HaftkaUniversity of FloridaPreverjeni e-poštni naslov na ufl.edu
Chanyoung ParkCaterpillar Inc.Preverjeni e-poštni naslov na cat.com
Nam Ho Kimuniversity of FloridaPreverjeni e-poštni naslov na ufl.edu
Gowri SrinivasanLos Alamos National LaboratoryPreverjeni e-poštni naslov na lanl.gov
Nishant PandaInformation Sciences, CCS-3, Los Alamos National LabPreverjeni e-poštni naslov na lanl.gov
Qingkai KongNorthern Illinois UniversityPreverjeni e-poštni naslov na math.niu.edu
S. BalachandarNewton C Ebaugh Professor of Mechanical & Aerospace EngineeringPreverjeni e-poštni naslov na ufl.edu
Xiaowei YUETsinghua UniversityPreverjeni e-poštni naslov na vt.edu
Daniel O'MalleyLos Alamos National LaboratoryPreverjeni e-poštni naslov na lanl.gov
Yinan WangRensselaer Polytechnic InstitutePreverjeni e-poštni naslov na rpi.edu
Cory B. ScottAssistant Professor of Computer Science, Colorado CollegePreverjeni e-poštni naslov na coloradocollege.edu
Diane OyenLos Alamos National LaboratoryPreverjeni e-poštni naslov na cs.unm.edu
Michael GrosskopfLos Alamos National LaboratoryPreverjeni e-poštni naslov na lanl.gov
Brandon WilsonUtah State UniversityPreverjeni e-poštni naslov na lanl.gov
Julia NakhlehGraduate Student, University of Wisconsin-MadisonPreverjeni e-poštni naslov na wisc.edu
Sylvain DubreuilONERA ToulousePreverjeni e-poštni naslov na onera.fr
Nathalie BartoliSenior researcher, ONERA - HDR Applied MathematicsPreverjeni e-poštni naslov na onera.fr
Weihong "Grace" GuoIndustrial and Systems Engineering at Rutgers UniversityPreverjeni e-poštni naslov na rutgers.edu
Abigail HunterLos Alamos National LaboratoryPreverjeni e-poštni naslov na lanl.gov
Ana Cristina AguiarLawrence Livermore National LaboratoryPreverjeni e-poštni naslov na llnl.gov

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M. Giselle Fernández-Godino

Research Scientist, Deputy Director DSICS, Lawrence Livermore National Laboratory

Preverjeni e-poštni naslov na llnl.gov - Domača stran

data science machine learning uncertainty quantification atmospheric science


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
Review of multi-fidelity models MG Fernández-Godino. Advances in Computational Science and Engineering 1 (4), 351-400, 2023	422*	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	209	2019
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	64	2021
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	23	2019
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	21	2021
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	14	2019
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	12	2021
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	12	2021
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	10	2016
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	9	2021
On the use of symmetries in building surrogate models M Giselle Fernández-Godino, S Balachandar, RT Haftka Journal of Mechanical Design 141 (6), 2019	9	2019
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	8	2023
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	7	2018
Review of multi-fidelity models (2016) MG Fernández-Godino, C Park, NH Kim, RT Haftka arXiv preprint arXiv:1609.07196, 2016	7	2016
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	6	2021
Review of multi-fidelity models M Giselle Fernández-Godino, C Park, NH Kim, RT Haftka arXiv e-prints, arXiv: 1609.07196, 2016	6	2016
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	6	2016
Review of multi-fidelity models. arXiv 2016 MG Fernández-Godino, C Park, NH Kim, RT Haftka arXiv preprint arXiv:1609.07196, 0	6
StressNet-Deep learning to predict stress with fracture propagation in brittle materials. npj Mater Degrad 5 Y Wang, D Oyen, WG Guo, A Mehta, CB Scott, N Panda, ...	5	2021
Quantifying particle departure from axisymmetry in multiphase cylindrical detonation MG Fernández-Godino Ph. D. Thesis, University of Florida, Gainesville, FL, 2018	5	2018

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