Jan Niklas Fuhg
Orcid: 0000-0002-5986-3770
According to our database1,
Jan Niklas Fuhg
authored at least 28 papers
between 2019 and 2025.
Collaborative distances:
Collaborative distances:
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Bibliography
2025
CoRR, September, 2025
Thermodynamically Consistent Hybrid and Permutation-Invariant Neural Yield Functions for Anisotropic Plasticity.
CoRR, August, 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