130 citations · 549 across the 28 of their papers we have counts for
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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…
Interval and fuzzy physics-informed neural networks for uncertain fields
Jan Niklas Fuhg, Ioannis Kalogeris, Amélie Fau +1
Temporally and spatially dependent uncertain parameters are regularly encountered in engineering applications. Commonly these uncertainties are accounted for using random fields an…
A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks
Teeratorn Kadeethum, Daniel O'Malley, Jan Niklas Fuhg +4
This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inve…
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…
Model-data-driven constitutive responses: application to a multiscale computational framework
Jan Niklas Fuhg, Christoph Boehm, Nikolaos Bouklas +3
Computational multiscale methods for analyzing and deriving constitutive responses have been used as a tool in engineering problems because of their ability to combine information…
Non-intrusive reduced order modeling of poroelasticity of heterogeneous media based on a discontinuous Galerkin approximation
T. Kadeethum, F. Ballarin, N. Bouklas
We present a non-intrusive model reduction framework for linear poroelasticity problems in heterogeneous porous media using proper orthogonal decomposition (POD) and neural network…