Nikolaos Bouklas

Publication Activity (10 Years)

Years Active: 2021-2024
Publications (10 Years): 22

Top Topics

Gaussian Process

Top Venues

J. Comput. Phys.

Comput. Geosci.

Nat. Comput. Sci.

Publications

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 (2024)
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 (2024)
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 (2023)
Jan Niklas Fuhg, Reese E. Jones, Nikolaos Bouklas
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. CoRR (2023)
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 (2023)
Jan Niklas Fuhg, Nikolaos Bouklas, Reese E. Jones
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen. CoRR (2023)
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 (2023)
Jan Niklas Fuhg, Nikolaos Bouklas
The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity. J. Comput. Phys. 451 (2022)
Jan Niklas Fuhg, Craig M. Hamel, Kyle Johnson, Reese Jones, Nikolaos Bouklas
Modular machine learning-based elastoplasticity: generalization in the context of limited data. CoRR (2022)
Jan Niklas Fuhg, Arnav Karmarkar, Teeratorn Kadeethum, Hongkyu Yoon, Nikolaos Bouklas
Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data. CoRR (2022)
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 (2022)
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 (2022)
Jan Niklas Fuhg, Nikolaos Bouklas
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity. CoRR (2021)
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) (2021)
Teeratorn Kadeethum, Francesco Ballarin, Y. Choi, Daniel O'Malley, H. Yoon, Nikolaos Bouklas
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. CoRR (2021)
Jan Niklas Fuhg, Michele Marino, Nikolaos Bouklas
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks. CoRR (2021)
Jan Niklas Fuhg, Amelie Fau, Nikolaos Bouklas
Interval and fuzzy physics-informed neural networks for uncertain fields. CoRR (2021)
Jan Niklas Fuhg, Nikolaos Bouklas
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. CoRR (2021)
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 (2021)
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 (2021)
Teeratorn Kadeethum, Dan O'Malley, Y. Choi, Hari S. Viswanathan, Nikolaos Bouklas, Hongkyu Yoon
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. CoRR (2021)
Teeratorn Kadeethum, Francesco Ballarin, Nikolaos Bouklas
Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. CoRR (2021)