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ORCIDNikolaos Bouklas
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2020 – today
- 2024
[i18]Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas, Bahador Bahmani, WaiChing Sun, Nikolaos N. Vlassis, Moritz Flaschel, Pietro Carrara, Laura De Lorenzis:
A review on data-driven constitutive laws for solids. CoRR abs/2405.03658 (2024)- [i17]Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta, Reese E. Jones, Nikolaos Bouklas:
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. CoRR abs/2407.00761 (2024) - 2023
- [j4]Teeratorn Kadeethum, John D. Jakeman
, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon:
Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. IEEE Access 11: 62970-62985 (2023) - [i16]Kshitiz Upadhyay, Jan Niklas Fuhg, Nikolaos Bouklas, K. T. Ramesh:
Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials. CoRR abs/2304.13897 (2023) - [i15]Adnan Eghtesad, Jan Niklas Fuhg, Nikolaos Bouklas:
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations. CoRR abs/2307.04301 (2023) - [i14]Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones:
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen. CoRR abs/2308.11080 (2023) - [i13]Jan Niklas Fuhg, Reese E. Jones, Nikolaos Bouklas:
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. CoRR abs/2310.03652 (2023) - 2022
- [j3]Teeratorn Kadeethum, Daniel O'Malley, Youngsoo Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon:
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. Comput. Geosci. 167: 105212 (2022) - [j2]Jan Niklas Fuhg, Nikolaos Bouklas:
The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity. J. Comput. Phys. 451: 110839 (2022) - [i12]Teeratorn Kadeethum, Francesco Ballarin, Daniel O'Malley, Youngsoo Choi, Nikolaos Bouklas, Hongkyu Yoon:
Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. CoRR abs/2202.05460 (2022) - [i11]Jan Niklas Fuhg, Arnav Karmarkar, Teeratorn Kadeethum, Hongkyu Yoon, Nikolaos Bouklas:
Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data. CoRR abs/2206.04675 (2022) - [i10]Jan Niklas Fuhg, Craig M. Hamel, Kyle Johnson, Reese E. Jones, Nikolaos Bouklas:
Modular machine learning-based elastoplasticity: generalization in the context of limited data. CoRR abs/2210.08343 (2022) - 2021
- [j1]Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas:
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. Nat. Comput. Sci. 1(12): 819-829 (2021) - [i9]Teeratorn Kadeethum, Francesco Ballarin, Nikolaos Bouklas:
Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. CoRR abs/2101.11810 (2021) - [i8]Jan Niklas Fuhg, Christoph Böhm, Nikolaos Bouklas, Amelie Fau, Peter Wriggers, Michele Marino:
Model-data-driven constitutive responses: application to a multiscale computational framework. CoRR abs/2104.02650 (2021) - [i7]Jan Niklas Fuhg, Nikolaos Bouklas:
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity. CoRR abs/2104.09623 (2021) - [i6]Jan Niklas Fuhg, Michele Marino, Nikolaos Bouklas:
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks. CoRR abs/2105.04554 (2021) - [i5]Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg, Youngsoo Choi, Jonghyun Lee, Hari S. Viswanathan, Nikolaos Bouklas:
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. CoRR abs/2105.13136 (2021) - [i4]Jan Niklas Fuhg, Amelie Fau, Nikolaos Bouklas:
Interval and fuzzy physics-informed neural networks for uncertain fields. CoRR abs/2106.13727 (2021) - [i3]Teeratorn Kadeethum, Francesco Ballarin, Youngsoo Choi, Daniel O'Malley, Hongkyu Yoon, Nikolaos Bouklas:
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. CoRR abs/2107.11460 (2021) - [i2]Jan Niklas Fuhg, Nikolaos Bouklas:
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. CoRR abs/2109.11028 (2021) - [i1]Teeratorn Kadeethum, Dan O'Malley, Youngsoo Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon:
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. CoRR abs/2111.14984 (2021)
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