The question of how methods from the field of artificial intelligence can help improve the
conventional frameworks for topology optimisation has received increasing attention over …

Topology optimization (TO) is a method of deriving an optimal design that satisfies a given
load and boundary conditions within a design domain. This method enables effective design …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …

A novel deep operator network (DeepONet) with a residual U-Net (ResUNet) as the trunk
network is devised to predict full-field highly nonlinear elastic–plastic stress response for …

Most existing geometry processing algorithms use meshes as the default shape
representation. Manipulating meshes, however, requires one to maintain high quality in the …

The structure of protein-protein complexes is critical for understanding binding dynamics,
biological mechanisms, and intervention strategies. Rigid protein docking, a fundamental …

Abstract Implicit Neural Spatial Representation (INSR) has emerged as an effective
representation of spatially-dependent vector fields. This work explores solving time …

The deep energy method (DEM) has been used to solve the elastic deformation of structures
with linear elasticity, hyperelasticity, and strain-gradient elasticity material models based on …

Bi-level optimization, especially the gradient-based category, has been widely used in the
deep learning community including hyperparameter optimization and meta-knowledge …