9 papers
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…
Multiscale topology optimization of compressible and nearly incompressible anisotropic hyperelastic structures using physics-augmented neural networks
Asghar A. Jadoon, Aryan Tyagi, L. River Spencer +5
Multiscale topology optimization (TO) of hyperelastic materials remains computationally prohibitive due to the repeated solution of microscale boundary value problems. In this work…
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…
Thermodynamically Consistent Hybrid and Permutation-Invariant Neural Yield Functions for Anisotropic Plasticity
Asghar A. Jadoon, Ravi G. Patel, Brian N. Granzow +3
Plastic anisotropy in metals remains challenging to model. This is partly because conventional phenomenological yield criteria struggle to combine a highly descriptive, flexible re…
A General, Automated Method for Building Structural Tensors of Arbitrary Order for Anisotropic Function Representations
Ravi G. Patel, Reese E. Jones, D. Thomas Seidl +2
We present a general, constructive procedure to find the basis for tensors of arbitrary order subject to linear constraints by transforming the problem to that of finding the nulls…
Differentiable neural network representation of multi-well, locally-convex potentials
Reese E. Jones, Adrian Buganza Tepole, Jan N. Fuhg
Multi-well potentials are ubiquitous in science, modeling phenomena such as phase transitions, dynamic instabilities, and multimodal behavior across physics, chemistry, and biology…