Jan Niklas Fuhg
Orcid: 0000-0002-5986-3770
According to our database1,
Jan Niklas Fuhg
authored at least 16 papers
between 2019 and 2023.
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Bibliography
2023
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.
CoRR, 2023
Stress representations for tensor basis neural networks: alternative formulations to Finger-Rivlin-Ericksen.
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