collaborators

7 papers

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…

cs.CE2025

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…

math-ph2025

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…

cs.LG2025

Physics Augmented Machine Learning Discovery of Composition-Dependent Constitutive Laws for 3D Printed Digital Materials

Steven Yang, Michal Levin, Govinda Anantha Padmanabha +6

Multi-material 3D printing, particularly through polymer jetting, enables the fabrication of digital materials by mixing distinct photopolymers at the micron scale within a single…

math.NA2025

A Note on the Reliability of Goal-Oriented Error Estimates for Galerkin Finite Element Methods with Nonlinear Functionals

Brian N. Granzow, Stephen D. Bond, D. Thomas Seidl +1

We consider estimating the discretization error in a nonlinear functional in the setting of an abstract variational problem: find such that $B(u,φ) = L(φ…

cs.CE2025

A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM

Sanjeev Kumar, D. Thomas Seidl, Brian N. Granzow +2

Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. The two primary approaches in the experimental mechanics' community fo…