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cond-mat.mtrl-sci2026
A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling
Reese E. Jones, Jan N. Fuhg
Recent advances in physics-augmented neural networks have enabled thermodynamically consistent data-driven constitutive modeling of complex inelastic materials. Most existing appro…
cond-mat.mtrl-sci2025
A physics-augmented neural network framework for modeling and detecting thermo-visco-plastic behavior
Reese E. Jones, Asghar Jadoon, D. Thomas Seidl +1
Although considerable attention has been devoted to the development of models for isothermal, rate-independent plasticity, many high-consequence performance assessments involve vis…
cond-mat.mtrl-sci2025
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…