6 citations · 17 across the 6 of their papers we have counts for
10 papers
An attention-based neural ordinary differential equation framework for modeling inelastic processes
Reese E. Jones, Jan N. Fuhg
To preserve strictly conservative behavior as well as model the variety of dissipative behavior displayed by solid materials, we propose a significant enhancement to the internal s…
A review on data-driven constitutive laws for solids
Jan Niklas Fuhg, Govinda Anantha Padmanabha, Nikolaos Bouklas +6
This review article highlights state-of-the-art data-driven techniques to discover, encode, surrogate, or emulate constitutive laws that describe the path-independent and path-depe…
Machine-learning convex and texture-dependent macroscopic yield from crystal plasticity simulations
Jan N. Fuhg, Lloyd van Wees, Mark Obstalecki +3
The influence of the microstructure of a polycrystalline material on its macroscopic deformation response is still one of the major problems in materials engineering. For materials…
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…
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…