Cikkek nyilvánosan hozzáférhető megbízással - Jan Niklas FuhgTovábbi információ
Valahol hozzáférhető: 15
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, 2689-2747, 2021
Megbízások: German Research Foundation
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
Megbízások: German Research Foundation
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, ...
Nature Computational Science 1 (12), 819-829, 2021
Megbízások: US Department of Energy
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
JN Fuhg, N Bouklas
Computer Methods in Applied Mechanics and Engineering 394, 114915, 2022
Megbízások: US National Science Foundation
Model-data-driven constitutive responses: Application to a multiscale computational framework
JN Fuhg, C Böhm, N Bouklas, A Fau, P Wriggers, M Marino
International Journal of Engineering Science 167, 103522, 2021
Megbízások: German Research Foundation, Government of Italy
Modular machine learning-based elastoplasticity: Generalization in the context of limited data
JN Fuhg, CM Hamel, K Johnson, R Jones, N Bouklas
Computer Methods in Applied Mechanics and Engineering 407, 115930, 2023
Megbízások: US Department of Energy, US Department of Defense
Learning hyperelastic anisotropy from data via a tensor basis neural network
JN Fuhg, N Bouklas, RE Jones
Journal of the Mechanics and Physics of Solids 168, 105022, 2022
Megbízások: US Department of Energy, US Department of Defense
Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations
JN Fuhg, L van Wees, M Obstalecki, P Shade, N Bouklas, M Kasemer
Materialia 23, 101446, 2022
Megbízások: US Department of Defense
Enhancing phenomenological yield functions with data: challenges and opportunities
JN Fuhg, A Fau, N Bouklas, M Marino
European Journal of Mechanics-A/Solids 99, 104925, 2023
Megbízások: US Department of Defense, Government of Italy
Enhancing high-fidelity nonlinear solver with reduced order model
T Kadeethum, D O’malley, F Ballarin, I Ang, JN Fuhg, N Bouklas, ...
Scientific reports 12 (1), 20229, 2022
Megbízások: US National Science Foundation, US Department of Energy, US Department of …
Deep convolutional Ritz method: parametric PDE surrogates without labeled data
JN Fuhg, A Karmarkar, T Kadeethum, H Yoon, N Bouklas
Applied Mathematics and Mechanics 44 (7), 1151-1174, 2023
Megbízások: US Department of Energy
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 105 (1), 277-299, 2021
Megbízások: German Research Foundation, Government of Italy
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
Megbízások: German Research Foundation
A classification-pursuing adaptive approach for Gaussian process regression on unlabeled data
JN Fuhg, A Fau
Mechanical Systems and Signal Processing 162, 107976, 2022
Megbízások: German Research Foundation
Computational Analysis of Coupled Geoscience Processes in Fractured and Deformable Media
H Yoon, A Kucala, KW Chang, MJ Martinez, JE Bean, T Kadeethum, ...
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2022
Megbízások: US Department of Energy
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