Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
It addresses the challenge of identifying yield functions in plasticity for materials scientists and engineers, offering a data-driven approach that avoids costly directional calibration and parametric assumptions.
This paper introduces a physics-informed framework to discover anisotropic yield functions from full-field displacement and reaction force data without direct stress or yield surface observations. The method, validated on FE benchmarks with von Mises, Hill 1948, and Yld2000-2d yield functions, accurately identifies yield contours and shows robustness to noise.
Identifying anisotropic yield functions remains challenging since yielding is not directly observed in full-field mechanical measurements, directional calibration can require many loading directions, and selecting an appropriate analytical form is nontrivial. This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and reaction force data, without stress observations, plastic strain measurements, direct yield surface data, or a prescribed parametric yield function. The framework identifies the yield function as a mechanically constrained constitutive component inside elastoplastic stress integration, rather than through direct stress-space supervision. The yield function is represented by a convex neural network that enforces convexity and positive homogeneity of degree one while imposing the assumed tension-compression symmetry, and this neural yield function is trained with a differentiable stress update and a physics-informed force equilibrium loss across multiple loading cases. The proposed framework is validated using finite element (FE) benchmark studies with von Mises, Hill 1948, and Yld2000-2d yield functions, assessing yield contour agreement, displacement-noise sensitivity, identifiability through plastically active stress states, epistemic uncertainty, and polynomial-surrogate deployment. This study provides a mechanics-constrained pathway for discovering anisotropic yield functions from displacement and force data while keeping the identified component within the structure of elastoplastic stress integration.