3 citations · 5 across the 10 of their papers we have counts for
11 papers
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 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…
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…
Inverse design of anisotropic microstructures using physics-augmented neural networks
Asghar A. Jadoon, Karl A. Kalina, Manuel K. Rausch +2
Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-…
Polyconvex neural network models of thermoelasticity
Jan N. Fuhg, Asghar Jadoon, Oliver Weeger +2
Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonl…
Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics
Jan N. Fuhg, Reese E. Jones, Nikolaos Bouklas
Data-driven constitutive modeling with neural networks has received increased interest in recent years due to its ability to easily incorporate physical and mechanistic constraints…