Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations

• URL: http://arxiv.org/abs/2606.19375v1
• Date: Fri, 12 Jun 2026 08:04:07 GMT
• Title: Physics-Informed Discovery of Yield Functions in Plasticity via Convex Neural Representations
• Abstract summary: This study proposes a physics-informed framework for discovering yield functions from full-field displacement data and force data.<n>The framework identifies the yield function as a mechanically constrained component inside elastoplastic stress integration.<n>The proposed framework is validated using finite element (FE) benchmark studies with von Mises, Hill 1948, and Yld2000-2d yield functions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Learning the Constitutive Behavior of Materials via Neural Operators and Causal Attention: Case Studies in Plasticity and Damage [0.0]
  We propose a data-driven modeling framework based on the concept of a material operator.<n>The model is trained directly on full loading paths as function-to-function mappings.<n>Results demonstrate accurate and robust predictions of irreversible deformation mechanisms.
  arXiv  Detail & Related papers  (2026-09-02T06:58:00Z)
• Rare Event Analysis via Stochastic Optimal Control [39.211234628277744]
  We introduce a framework that casts committor estimation as an optimal control (SOC) problem.<n>We develop two complementary objectives: a direct backpropagation loss and a principled off-policy Value Matching loss.<n>On benchmark systems, the framework yields more accurate committor estimates, reaction rates, and equilibrium constants than existing methods.
  arXiv  Detail & Related papers  (2026-04-14T18:34:38Z)
• Discovery of interaction and diffusion kernels in particle-to-mean-field multi-agent systems [41.99844472131922]
  We propose a data-driven framework to learn interaction kernels in multi-agent systems.<n>Our approach aims at identifying the functional form of nonlocal interaction and diffusion terms directly from trajectory data.
  arXiv  Detail & Related papers  (2026-03-16T21:19:25Z)
• Adaptive Manipulation Potential and Haptic Estimation for Tool-Mediated Interaction [14.888648782445694]
  This paper introduces a parameterized Equilibrium Manifold (EM) as a unified representation for tool-mediated interaction.<n>We develop a closed-loop framework that integrates haptic estimation, online planning, and adaptive stiffness control.<n>The framework is validated through simulation and over 260 real-world screw-loosening trials.
  arXiv  Detail & Related papers  (2026-03-11T02:57:55Z)
• Equivariant Evidential Deep Learning for Interatomic Potentials [55.6997213490859]
  Uncertainty quantification is critical for assessing the reliability of machine learning interatomic potentials in molecular dynamics simulations.<n>Existing UQ approaches for MLIPs are often limited by high computational cost or suboptimal performance.<n>We propose textitEquivariant Evidential Deep Learning for Interatomic Potentials ($texte2$IP), a backbone-agnostic framework that models atomic forces and their uncertainty jointly.
  arXiv  Detail & Related papers  (2026-02-11T02:00:25Z)
• Deep Deterministic Nonlinear ICA via Total Correlation Minimization with Matrix-Based Entropy Functional [41.05541240448253]
  Blind source separation, particularly through independent component analysis (ICA), is widely utilized across various signal processing domains.<n>We present deep deterministic nonlinear independent component analysis (DDICA), a novel deep neural network-based framework designed to address these limitations.<n>We validated DDICA across a range of applications, including simulated signal mixtures, hyperspectral image unmixing, modeling of primary visual receptive fields, and resting-state functional magnetic resonance imaging (fMRI) data analysis.
  arXiv  Detail & Related papers  (2025-12-31T19:44:19Z)
• PROD: Palpative Reconstruction of Deformable Objects through Elastostatic Signed Distance Functions [0.0]
  PROD (Palpative Reconstruction of Deformables) is a novel method for reconstructing the shape and mechanical properties of deformable objects.<n>We model the deformation of an object as an elastostatic process and derive a governing Poisson equation for estimating its SDF.<n>We demonstrate the robustness of PROD in handling pose errors, non-normal force application, and curvature errors in simulated soft body interactions.
  arXiv  Detail & Related papers  (2025-08-18T01:13:58Z)
• Fully data-driven inverse hyperelasticity with hyper-network neural ODE fields [0.0]
  We propose a new framework for identifying mechanical properties of heterogeneous materials without a closed-form equation.<n>A physics-based data-driven method built upon ordinary neural differential equations (NODEs) is employed to discover equations.<n>The proposed approach is robust and general in identifying the mechanical properties of heterogeneous materials with very few assumptions.
  arXiv  Detail & Related papers  (2025-06-09T18:50:14Z)
• Analog In-memory Training on General Non-ideal Resistive Elements: The Impact of Response Functions [46.75046795995564]
  In AIMC hardware, the trainable weights are represented by the conductance of resistive elements and updated using consecutive electrical pulses.<n>While the conductance changes by a constant in response to each pulse, in reality, the change is scaled by asymmetric and non-linear response functions, leading to a non-ideal training dynamic.<n>This paper provides a theoretical foundation for gradient-based training on AIMC hardware with non-ideal response functions.
  arXiv  Detail & Related papers  (2025-02-10T09:56:15Z)
• Use of Deep Neural Networks for Uncertain Stress Functions with Extensions to Impact Mechanics [9.73713941604395]
  We propose a deep neural network approach to model stress as a state function with quantile regression to capture uncertainty. We extend these models to uniaxial impact mechanics using differential equations to demonstrate a use case and provide a framework for implementing this uncertainty-aware stress function.
  arXiv  Detail & Related papers  (2023-11-03T00:12:24Z)
• Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA [81.4991350761909]
  Independent component analysis (ICA) refers to an ensemble of methods which formalize this goal and provide estimation procedure for practical application. We show that the latent variables can be recovered up to a permutation if one regularizes the latent mechanisms to be sparse.
  arXiv  Detail & Related papers  (2021-07-21T14:22:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.