Nikolaos Bouklas
Orcid: 0000-0002-3349-5914
According to our database1,
Nikolaos Bouklas
authored at least 17 papers
between 2021 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
Epistemic Uncertainty-Aware Barlow Twins Reduced Order Modeling for Nonlinear Contact Problems.
IEEE Access, 2023
2022
The mixed Deep Energy Method for resolving concentration features in finite strain hyperelasticity.
J. Comput. Phys., 2022
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties.
Comput. Geosci., 2022
Modular machine learning-based elastoplasticity: generalization in the context of limited data.
CoRR, 2022
CoRR, 2022
Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds.
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
Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: comparison with linear subspace techniques.
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
Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation.
CoRR, 2021