130 citations · 549 across the 28 of their papers we have counts for
4 papers · 2 filters
Continuous conditional generative adversarial networks for data-driven solutions of poroelasticity with heterogeneous material properties
T. Kadeethum, D. O'Malley, Y. Choi +3
Machine learning-based data-driven modeling can allow computationally efficient time-dependent solutions of PDEs, such as those that describe subsurface multiphysical problems. In…
On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling
Jan Niklas Fuhg, Nikolaos Bouklas
Data-driven constitutive modeling is an emerging field in computational solid mechanics with the prospect of significantly relieving the computational costs of hierarchical computa…
Local approximate Gaussian process regression for data-driven constitutive laws: Development and comparison with neural networks
Jan Niklas Fuhg, Michele Marino, Nikolaos Bouklas
Hierarchical computational methods for multiscale mechanics such as the FE and FE-FFT methods are generally accompanied by high computational costs. Data-driven approaches are…
The mixed deep energy method for resolving concentration features in finite strain hyperelasticity
Jan N. Fuhg, Nikolaos Bouklas
The introduction of Physics-informed Neural Networks (PINNs) has led to an increased interest in deep neural networks as universal approximators of PDEs in the solid mechanics comm…