Παρακολούθηση
Jan Niklas Fuhg
Jan Niklas Fuhg
Άλλα ονόματαJan N. Fuhg, Jan Fuhg
UT Austin
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα utexas.edu - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
Έτος
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
2312021
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
1572020
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
JN Fuhg, N Bouklas
Journal of Computational Physics 451, 110839, 2022
1052022
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
962021
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
922022
Local approximate Gaussian process regression for data-driven constitutive models: development and comparison with neural networks
JN Fuhg, M Marino, N Bouklas
Computer Methods in Applied Mechanics and Engineering 388, 114217, 2022
682022
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
672021
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
422023
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
412022
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
412022
A review on data-driven constitutive laws for solids
JN Fuhg, G Anantha Padmanabha, N Bouklas, B Bahmani, WC Sun, ...
Archives of Computational Methods in Engineering, 1-43, 2024
252024
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
25*2023
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
212022
Interval and fuzzy physics-informed neural networks for uncertain fields
JN Fuhg, A Fau, N Bouklas
212021
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
JN Fuhg, RE Jones, N Bouklas
Computer Methods in Applied Mechanics and Engineering 426, 116973, 2024
182024
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
172023
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
162021
Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials
K Upadhyay, JN Fuhg, N Bouklas, KT Ramesh
Computational Mechanics, 1-30, 2024
152024
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
112019
Adaptive surrogate models for parametric studies
JN Fuhg
arXiv preprint arXiv:1905.05345, 2019
112019
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