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 …

Develo** accurate yet fast computational tools to simulate complex physical phenomena
is a long-standing problem. Recent advances in machine learning have revolutionized the …

Phase-field modeling is an effective but computationally expensive method for capturing the
mesoscale morphological and microstructure evolution in materials. Hence, fast and …

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 …

Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …

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 …

The mechanical properties of strut-based lattice structures are greatly influenced by cell
topology, which can be modified by changing connections between nodes within a single …

Finite element method (FEM) based high-fidelity simulation can be computationally
demanding and time-consuming as engineering problems become more complicated. It is …

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 …

Abstract Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to
simulate complex multiphysics problems with accelerated performance times. However …