Jan N. Fuhg
Jan N. Fuhg
Bestätigte E-Mail-Adresse bei cornell.edu
Titel
Zitiert von
Zitiert von
Jahr
A machine learning based plasticity model using proper orthogonal decomposition
D Huang, JN Fuhg, C Weißenfels, P Wriggers
Computer Methods in Applied Mechanics and Engineering 365, 113008, 2020
282020
State-of-the-art and comparative review of adaptive sampling methods for kriging
JN Fuhg, A Fau, U Nackenhorst
Archives of Computational Methods in Engineering 28 (4), 2689-2747, 2021
152021
Model-data-driven constitutive responses: application to a multiscale computational framework
JN Fuhg, C Böhm, N Bouklas, A Fau, P Wriggers, M Marino
arXiv preprint arXiv:2104.02650, 2021
52021
Adaptive surrogate models for parametric studies
JN Fuhg
arXiv preprint arXiv:1905.05345, 2019
52019
Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing’s type
JN Fuhg, A Fau
Nonlinear Dynamics 98 (3), 1709-1729, 2019
42019
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
JN Fuhg, N Bouklas
arXiv preprint arXiv:2104.09623, 2021
32021
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
T Kadeethum, D O'Malley, JN Fuhg, Y Choi, J Lee, HS Viswanathan, ...
arXiv preprint arXiv:2105.13136, 2021
22021
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks
JN Fuhg, M Marino, N Bouklas
arXiv preprint arXiv:2105.04554, 2021
22021
An innovative adaptive kriging approach for efficient binary classification of mechanical problems
JN Fuhg, A Fau
arXiv preprint arXiv:1907.01490, 2019
22019
A classification-pursuing adaptive approach for Gaussian process regression on unlabeled data
JN Fuhg, A Fau
Mechanical Systems and Signal Processing 162, 107976, 2022
12022
PI/PID controller stabilizing sets of uncertain nonlinear systems: an efficient surrogate model-based approach
JH Urrea-Quintero, JN Fuhg, M Marino, A Fau
Nonlinear Dynamics, 1-23, 2021
12021
INTERVAL AND FUZZY PHYSICS-INFORMED NEURAL NETWORKS FOR UNCERTAIN FIELDS
JN Fuhg, A Fau, N Bouklas
2021
Adaptive surrogate models for parametric studies Adaptive Ersatzmodelle für parametrische Studien
JN Fuhg, IA Fau, IU Nackenhorst
2019
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