2 citations · 3 across the 10 of their papers we have counts for
12 papers
Mixture of Polyconvex Neural Potentials for Parametric Hyperelasticity: Towards Foundation Material Models
Steven J. Yang, Govinda Anantha Padmanabha, D. Thomas Seidl +1
Hyperelastic constitutive models enable modeling large deformations in elastic solids. In common practice, a strain energy density function is prescribed in advance and model-speci…
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan +4
Constitutive modeling under uncertainty remains a central challenge for reliable mechanics simulations, particularly when the available stress-deformation data are sparse, noisy, o…
Full-Field Calibration of Coupled Thermomechanical Material Models at Finite Strain
L. River Spencer, William D. Meador, Adrian Buganza Tepole +5
Calibrating thermomechanical material models from experiments is challenging because deformation, temperature, and force responses are strongly coupled, while measurements are usua…
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…
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…
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…
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…
A Direct-adjoint Approach for Material Point Model Calibration with Application to Plasticity
Ryan Yan, D. Thomas Seidl, Reese E. Jones +1
This paper proposes a new approach for the calibration of material parameters in local elastoplastic constitutive models. The calibration is posed as a constrained optimization pro…
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…