Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation. - Teeratorn Kadeethum; Francesco Ballarin; Nikolaos Bouklas (CoRR, 2021)

Model-data-driven constitutive responses: application to a multiscale computational framework. - Jan Niklas Fuhg; Christoph Böhm 005; Nikolaos Bouklas; Amelie Fau; Peter Wriggers; Michele Marino (CoRR, 2021)

Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks. - Jan Niklas Fuhg; Michele Marino; Nikolaos Bouklas (CoRR, 2021)

Interval and fuzzy physics-informed neural networks for uncertain fields. - Jan Niklas Fuhg; Amelie Fau; Nikolaos Bouklas (CoRR, 2021)

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques. - Teeratorn Kadeethum; Francesco Ballarin; Youngsoo Choi; Daniel O'Malley; Hongkyu Yoon; Nikolaos Bouklas (CoRR, 2021)

On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling. - Jan Niklas Fuhg; Nikolaos Bouklas (CoRR, 2021)

A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks. - Teeratorn Kadeethum; Daniel O'Malley; Jan Niklas Fuhg; Youngsoo Choi; Jonghyun Lee 005; Hari S. Viswanathan; Nikolaos Bouklas (Nat. Comput. Sci., 2021)

Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds. - Teeratorn Kadeethum; Francesco Ballarin; Daniel O'Malley; Youngsoo Choi; Nikolaos Bouklas; Hongkyu Yoon (CoRR, 2022)

Deep Convolutional Ritz Method: Parametric PDE surrogates without labeled data. - Jan Niklas Fuhg; Arnav Karmarkar; Teeratorn Kadeethum; Hongkyu Yoon; Nikolaos Bouklas (CoRR, 2022)

Modular machine learning-based elastoplasticity: generalization in the context of limited data. - Jan Niklas Fuhg; Craig M. Hamel; Kyle Johnson; Reese E. Jones; Nikolaos Bouklas (CoRR, 2022)

Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties. - Teeratorn Kadeethum; Daniel O'Malley; Youngsoo Choi; Hari S. Viswanathan; Nikolaos Bouklas; Hongkyu Yoon (Comput. Geosci., 2022)

The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity. - Jan Niklas Fuhg; Nikolaos Bouklas (J. Comput. Phys., 2022)

Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems. - Teeratorn Kadeethum; John D. Jakeman; Youngsoo Choi; Nikolaos Bouklas; Hongkyu Yoon (IEEE Access, 2023)

Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials. - Kshitiz Upadhyay; Jan Niklas Fuhg; Nikolaos Bouklas; K. T. Ramesh (CoRR, 2023)

NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations. - Adnan Eghtesad; Jan Niklas Fuhg; Nikolaos Bouklas (CoRR, 2023)

Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics. - Jan Niklas Fuhg; Reese E. Jones; Nikolaos Bouklas (CoRR, 2023)

A review on data-driven constitutive laws for solids. - Jan Niklas Fuhg; Govinda Anantha Padmanabha; Nikolaos Bouklas; Bahador Bahmani; WaiChing Sun; Nikolaos N. Vlassis; Moritz Flaschel; Pietro Carrara; Laura De Lorenzis (CoRR, 2024)

Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models. - Govinda Anantha Padmanabha; Jan Niklas Fuhg; Cosmin Safta; Reese E. Jones; Nikolaos Bouklas (CoRR, 2024)

Condensed Stein Variational Gradient Descent for Uncertainty Quantification of Neural Networks. - Govinda Anantha Padmanabha; Cosmin Safta; Nikolaos Bouklas; Reese E. Jones (CoRR, 2024)

Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger-Rivlin-Ericksen. - Jan Niklas Fuhg; Nikolaos Bouklas; Reese E. Jones (J. Comput. Inf. Sci. Eng., 2024)

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials. - Steven Yang; Michal Levin; Govinda Anantha Padmanabha; Miriam Borshevsky; Ohad Cohen; D. Thomas Seidl; Reese E. Jones; Nikolaos Bouklas; Noy Cohen (CoRR, 2025)