Additive manufacturing (AM) processes have proven to be a perfect match for topology
optimization (TO), as they are able to realize sophisticated geometries in a unique layer-by …

Hybrid additive–subtractive manufacturing (HASM) is a revolutionary technique that
fabricates complex-shaped parts in high precision. One setup to finish complicated …

In this paper, an efficient deep unsupervised learning (DUL)-based framework is proposed
to directly perform the design optimization of truss structures under multiple constraints for …

In this paper, we propose a novel graded multiscale topology optimization framework by
exploiting the unique classification capacity of neural networks. The salient features of this …

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM)
library. Constructed on top of Google JAX, a rising machine learning library focusing on high …

Structural topology optimization is a compute-intensive process due to several iterations of
simulations required to evaluate the performance of the component during optimization …

This paper explores the possibilities of applying physics-informed neural networks (PINNs)
in topology optimization (TO) by introducing a fully self-supervised TO framework based on …

In this paper, an efficient Force Neural Network (FNN) is developed to reformulate the size
optimization of truss structures as an operator learning problem. A Deep Neural Network …

In this paper, we present a topology optimization (TO) framework to simultaneously optimize
the matrix topology and fiber distribution of functionally graded continuous fiber-reinforced …

Fluid-flow devices with low dissipation, but large contact area, are of importance in many
applications. A well-known strategy to design such devices is multi-scale topology …