3 citations · 5 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Input Specific Neural Networks
Asghar A. Jadoon, D. Thomas Seidl, Reese E. Jones +1
The black-box nature of neural networks limits the ability to encode or impose specific structural relationships between inputs and outputs. While various studies have introduced a…
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…