Jan Niklas Fuhg
Orcid: 0000-0002-5986-3770
According to our database1,
Jan Niklas Fuhg
authored at least 26 papers
between 2019 and 2025.
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Bibliography
2025
Graph Neural Network Surrogates for Contacting Deformable Bodies with Necessary and Sufficient Contact Detection.
CoRR, July, 2025
Differentiable neural network representation of multi-well, locally-convex potentials.
CoRR, June, 2025
CoRR, June, 2025
A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM.
CoRR, March, 2025
Polyconvex Physics-Augmented Neural Network Constitutive Models in Principal Stretches.
CoRR, March, 2025
2024
Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger-Rivlin-Ericksen.
J. Comput. Inf. Sci. Eng., 2024
Inverse design of anisotropic microstructures using physics-augmented neural networks.
CoRR, 2024
Automated model discovery of finite strain elastoplasticity from uniaxial experiments.
CoRR, 2024
Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models.
CoRR, 2024
2023
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.
CoRR, 2023
NN-EVP: A physics informed neural network-based elasto-viscoplastic framework for predictions of grain size-aware flow response under large deformations.
CoRR, 2023
Physics-informed Data-driven Discovery of Constitutive Models with Application to Strain-Rate-sensitive Soft Materials.
CoRR, 2023
2022
The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity.
J. Comput. Phys., 2022
Modular machine learning-based elastoplasticity: generalization in the context of limited data.
CoRR, 2022
CoRR, 2022
2021
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks.
Nat. Comput. Sci., 2021
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling.
CoRR, 2021
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks.
CoRR, 2021
Model-data-driven constitutive responses: application to a multiscale computational framework.
CoRR, 2021
2020
CoRR, 2020
2019
Surrogate model approach for investigating the stability of a friction-induced oscillator of Duffing's type.
CoRR, 2019
An innovative adaptive kriging approach for efficient binary classification of mechanical problems.
CoRR, 2019