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cs.CE2026

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…

cs.CE2026

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…

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…

cs.CE2025

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…

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…