Deep learning in computational mechanics: a review
L Herrmann, S Kollmannsberger - Computational Mechanics, 2024 - Springer
The rapid growth of deep learning research, including within the field of computational
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
mechanics, has resulted in an extensive and diverse body of literature. To help researchers …
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Phase-field modeling is an effective but computationally expensive method for capturing the
mesoscale morphological and microstructure evolution in materials. Hence, fast and …
mesoscale morphological and microstructure evolution in materials. Hence, fast and …
A review of graph neural network applications in mechanics-related domains
Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …
and physical representations, particularly for non-uniform structures. Graph neural networks …
Predicting stress, strain and deformation fields in materials and structures with graph neural networks
Developing accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …
is a long-standing problem. Recent advances in machine learning have revolutionized the …
Towards high-accuracy axial springback: Mesh-based simulation of metal tube bending via geometry/process-integrated graph neural networks
Springback has always been a stubborn defect that affects the axial accuracy of metal
bending. The finite element simulation of springback enables effective control and precise …
bending. The finite element simulation of springback enables effective control and precise …
Prediction and control of fracture paths in disordered architected materials using graph neural networks
K Karapiperis, DM Kochmann - Communications Engineering, 2023 - nature.com
Architected materials typically rely on regular periodic patterns to achieve improved
mechanical properties such as stiffness or fracture toughness. Here we introduce a class of …
mechanical properties such as stiffness or fracture toughness. Here we introduce a class of …
[HTML][HTML] Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods
A hybrid methodology based on numerical and non-destructive experimental schemes,
which is able to predict the structural level strength of composite laminates is proposed on …
which is able to predict the structural level strength of composite laminates is proposed on …
Graph Neural Networks (GNNs) based accelerated numerical simulation
C Jiang, NZ Chen - Engineering Applications of Artificial Intelligence, 2023 - Elsevier
Finite element method (FEM) based high-fidelity simulation can be computationally
demanding and time-consuming as engineering problems become more complicated. It is …
demanding and time-consuming as engineering problems become more complicated. It is …
Learning dislocation dynamics mobility laws from large-scale MD simulations
By dispensing with all the atoms and only focusing on dislocation lines, the computational
method of Discrete Dislocation Dynamics (DDD) gains greatly over Molecular Dynamics …
method of Discrete Dislocation Dynamics (DDD) gains greatly over Molecular Dynamics …
A generalized machine learning framework for brittle crack problems using transfer learning and graph neural networks
Despite their recent success, machine learning (ML) models such as graph neural networks
(GNNs), suffer from drawbacks such as the need for large training datasets and poor …
(GNNs), suffer from drawbacks such as the need for large training datasets and poor …