Jan Niklas Fuhg

Citado por

	Total	Desde 2019
Citas	795	795
Índice h	12	12
Índice i10	16	16

380

190

285

2019202020212022202320242 8 50 186 377 171

Acceso público

Ver todo

14 artículos

0 artículos

disponibles

no disponibles

Basado en requisitos de financiación

Coautores

Nikolaos BouklasAssociate Professor, Cornell UniversityDirección de correo verificada de cornell.edu
Amélie FauLMPS (Laboratoire de Mécanique Paris Saclay) - École Normale Supérieure Paris-SaclayDirección de correo verificada de ens-paris-saclay.fr
Michele MarinoDepartment of Civil Engineering and Computer Science, University of Rome Tor VergataDirección de correo verificada de ing.uniroma2.it
Teeratorn KadeethumSandia National LaboratoriesDirección de correo verificada de sandia.gov
Peter WriggersProfessor für Mechanik, Leibniz Universität HannoverDirección de correo verificada de ikm.uni-hannover.de
Reese E. JonesSandia National LaboratoriesDirección de correo verificada de sandia.gov
Daniel O'MalleyLos Alamos National LaboratoryDirección de correo verificada de lanl.gov
Hari ViswanathanSenior Scientist, Los Alamos National LaboratoryDirección de correo verificada de lanl.gov
Udo NackenhorstProfessor für Mechanik, Leibniz Universität HannoverDirección de correo verificada de ibnm.uni-hannover.de
Christian WeißenfelsProfessor for Mechanics, University of AugsburgDirección de correo verificada de mrm.uni-augsburg.de
Hongkyu Yoonsandia national laboratoriesDirección de correo verificada de sandia.gov
Matthew KasemerUniversity of AlabamaDirección de correo verificada de eng.ua.edu
Jonghyun "Harry" LeeUniversity of Hawaii at ManoaDirección de correo verificada de hawaii.edu
Youngsoo ChoiResearch Scientist, LLNLDirección de correo verificada de llnl.gov
Paul A ShadeAir Force Research LaboratoryDirección de correo verificada de us.af.mil

Seguir

Jan Niklas Fuhg

Otros nombresJan N. Fuhg, Jan Fuhg

UT Austin

Dirección de correo verificada de utexas.edu - Página principal

Solid mechanics Plasticity Physics-informed machine learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	170	2021
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	133	2020
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity JN Fuhg, N Bouklas Journal of Computational Physics 451, 110839, 2022	76	2022
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	66	2022
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	66	2021
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	50	2022
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	50	2021
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	31	2022
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	27	2022
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	25	2023
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	18*	2023
Interval and fuzzy physics-informed neural networks for uncertain fields JN Fuhg, A Fau, N Bouklas	15	2021
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	12	2021
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	11	2022
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	11	2019
Adaptive surrogate models for parametric studies JN Fuhg arXiv preprint arXiv:1905.05345, 2019	11	2019
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	7	2023
A classification-pursuing adaptive approach for Gaussian process regression on unlabeled data JN Fuhg, A Fau Mechanical Systems and Signal Processing 162, 107976, 2022	7	2022
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen J Fuhg, N Bouklas, R Jones Journal of Computing and Information Science in Engineering, 1-39, 2024	3	2024
Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials K Upadhyay, JN Fuhg, N Bouklas, KT Ramesh arXiv preprint arXiv:2304.13897, 2023	3	2023

El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.

Artículos 1–20

Citas por año

Citas duplicadas

Citas combinadas

Añadir coautoresCoautores

Seguir

Citado por

Coautores