Adaptive surrogate models for parametric studies. - Jan Niklas Fuhg (CoRR, 2019)

An innovative adaptive kriging approach for efficient binary classification of mechanical problems. - Jan Niklas Fuhg; Amelie Fau (CoRR, 2019)

Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing's type. - Jan Niklas Fuhg; Amelie Fau (CoRR, 2019)

A machine learning based plasticity model using proper orthogonal decomposition. - Dengpeng Huang; Jan Niklas Fuhg; Christian Weißenfels; Peter Wriggers (CoRR, 2020)

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)

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)

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)

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

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)

Automated model discovery of finite strain elastoplasticity from uniaxial experiments. - Asghar A. Jadoon; Knut A. Meyer; Jan Niklas Fuhg (CoRR, 2024)

Inverse design of anisotropic microstructures using physics-augmented neural networks. - Asghar A. Jadoon; Karl A. Kalina; Manuel K. Rausch; Reese E. Jones; Jan Niklas Fuhg (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)

Input Specific Neural Networks. - Asghar A. Jadoon; D. Thomas Seidl; Reese E. Jones; Jan Niklas Fuhg (CoRR, 2025)

Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretches. - Adrian Buganza Tepole; Asghar A. Jadoon; Manuel K. Rausch; Jan Niklas Fuhg (CoRR, 2025)

A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM. - Sanjeev Kumar; D. Thomas Seidl; Brian Neal Granzow; Jin Yang; Jan Niklas Fuhg (CoRR, 2025)

Bubble Dynamics Transformer: Microrheology at Ultra-High Strain Rates. - Lehu Bu; Zhaohan Yu; Shaoting Lin; Jan Niklas Fuhg; Jin Yang (CoRR, 2025)

Differentiable neural network representation of multi-well, locally-convex potentials. - Reese E. Jones; Adrian Buganza Tepole; Jan Niklas Fuhg (CoRR, 2025)

Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection. - Vijay K. Dubey; Collin E. Haese; Osman Gültekin; David Dalton; Manuel K. Rausch; Jan Niklas Fuhg (CoRR, 2025)