| 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 | 169 | 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 | 30 | 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 | 26 | 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 | 24 | 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 |
| 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 |
| 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 | 2 | 2024 |
Nikolaos BouklasAssociate Professor, Cornell UniversityVerified email at cornell.edu
