A review on data-driven constitutive laws for solids
This review article highlights state-of-the-art data-driven techniques to discover, encode,
surrogate, or emulate constitutive laws that describe the path-independent and path …
surrogate, or emulate constitutive laws that describe the path-independent and path …
Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications
H Hu, L Qi, X Chao - Thin-Walled Structures, 2024 - Elsevier
For solving the computational solid mechanics problems, despite significant advances have
been achieved through the numerical discretization of partial differential equations (PDEs) …
been achieved through the numerical discretization of partial differential equations (PDEs) …
Exploring energy minimization to model strain localization as a strong discontinuity using Physics Informed Neural Networks
O León, V Rivera, A Vázquez-Patiño, J Ulloa… - Computer Methods in …, 2025 - Elsevier
We explore the possibilities of using energy minimization for the numerical modeling of
strain localization in solids as a sharp discontinuity in the displacement field. For this …
strain localization in solids as a sharp discontinuity in the displacement field. For this …
The novel graph transformer-based surrogate model for learning physical systems
B Feng, XP Zhou - Computer Methods in Applied Mechanics and …, 2024 - Elsevier
Predicting physical systems over long-term horizons has a significant challenge. Although
prevalent machine learning techniques, such as Physics-Informed Neural Networks (PINNs) …
prevalent machine learning techniques, such as Physics-Informed Neural Networks (PINNs) …
DEDEM: Discontinuity Embedded Deep Energy Method for solving fracture mechanics problems
L Zhao, Q Shao - arXiv preprint arXiv:2407.11346, 2024 - arxiv.org
Physics-Informed Neural Networks (PINNs) have aroused great attention for its ability to
address forward and inverse problems of partial differential equations. However …
address forward and inverse problems of partial differential equations. However …
A novel graph networks based learnable physics engines for crack propagation and coalescence in solid mechanics
K Feng, XP Zhou - Engineering Fracture Mechanics, 2025 - Elsevier
Simulating evolution process of physical systems is notoriously difficult due to complex
geometry and the strong nonlinearity of interactions (eg containing cracks). In this work, a …
geometry and the strong nonlinearity of interactions (eg containing cracks). In this work, a …
Predicting Crack Nucleation and Propagation in Brittle Materials Using Deep Operator Networks with Diverse Trunk Architectures
Phase-field modeling reformulates fracture problems as energy minimization problems and
enables a comprehensive characterization of the fracture process, including crack …
enables a comprehensive characterization of the fracture process, including crack …
Finite-PINN: A Physics-Informed Neural Network Architecture for Solving Solid Mechanics Problems with General Geometries
PINN models have demonstrated impressive capabilities in addressing fluid PDE problems,
and their potential in solid mechanics is beginning to emerge. This study identifies two key …
and their potential in solid mechanics is beginning to emerge. This study identifies two key …
Exploring the ability of the Deep Ritz Method to model strain localization as a sharp discontinuity
O León, V Rivera, A Vázquez-Patiño, J Ulloa… - arXiv preprint arXiv …, 2024 - arxiv.org
We present an exploratory study of the possibilities of the Deep Ritz Method (DRM) for the
modeling of strain localization in solids as a sharp discontinuity in the displacement field. For …
modeling of strain localization in solids as a sharp discontinuity in the displacement field. For …