About

Jan N. Fuhg is an Assistant Professor at the University of Texas at Austin. The available page information includes links to his ResearchGate, LinkedIn, Github, Google Scholar, and ORCID profiles, indicating his active engagement in academic and professional networks. The page also references his role within a team and provides contact and follow options, but does not include specific details about his research focus, background, or key contributions. Therefore, no detailed biography content is available from the provided text.

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Geometry
• Materials science
• Pure mathematics
• Computational chemistry
• Chemistry
• Statistical physics
• Physics
• Algorithm
• Applied mathematics
• Composite material

Selected publications

• Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

  Journal of Computing and Information Science in Engineering · 2024 · 13 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Abstract Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance. In these models, the stress prediction follows from a linear combination of invariant-dependent coefficient functions and known tensor basis generators. However, thus far the formulations have been limited to stress representations based on the classical Finger–Rivlin–Ericksen form, while the performance of alternative representations has yet to be investigated. In this work, we survey a variety of tensor basis neural network models for modeling hyperelastic materials in a finite deformation context, including a number of so far unexplored formulations which use theoretically equivalent invariants and generators to Finger–Rivlin–Ericksen. Furthermore, we compare potential-based and coefficient-based approaches, as well as different calibration techniques. Nine variants are tested against both noisy and noiseless datasets for three different materials. Theoretical and practical insights into the performance of each formulation are given.

  DOI

• Multiscale simulation of spatially correlated microstructure via a latent space representation

  International Journal of Solids and Structures · 2024 · 9 citations

  • Computer Science
  • Statistical physics
  • Materials science
  DOI

• Polyconvex Neural Network Models of Thermoelasticity

  2024 · 2 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence
  DOI

Frequent coauthors

• Reese E. Jones

  Sandia National Laboratories California

  5 shared

• Nikolaos Bouklas

  Cornell University

  5 shared

• Craig M. Hamel

  3 shared

• Dan Bolintineanu

  Sandia National Laboratories

  3 shared

• Kyle Johnson

  3 shared

• Sharlotte Kramer

  Sandia National Laboratories

  3 shared

• Manuel K. Rausch

  2 shared

• Tomasz A. Timek

  2 shared

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