On the use of artificial neural networks in topology optimisation
RV Woldseth, N Aage, JA Bærentzen… - Structural and …, 2022 - Springer
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 …
conventional frameworks for topology optimisation has received increasing attention over …
Computational design of mechanical metamaterials
In the past few years, design of mechanical metamaterials has been empowered by
computational tools that have allowed the community to overcome limitations of human …
computational tools that have allowed the community to overcome limitations of human …
Aligning optimization trajectories with diffusion models for constrained design generation
G Giannone, A Srivastava… - Advances in Neural …, 2023 - proceedings.neurips.cc
Generative models have significantly influenced both vision and language domains,
ushering in innovative multimodal applications. Although these achievements have …
ushering in innovative multimodal applications. Although these achievements have …
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 …
Graph neural networks and implicit neural representation for near-optimal topology prediction over irregular design domains
This paper proposes a deep neural network-based topology optimization acceleration
method for irregular design domains that predicts (near-) optimal topologies. A topology …
method for irregular design domains that predicts (near-) optimal topologies. A topology …
Dynamic graph-based convergence acceleration for topology optimization in unstructured meshes
Topology optimization need acceleration to reduce computational costs. While various
efforts have employed Convolutional Neural Networks (CNNs) for this purpose, they aren't …
efforts have employed Convolutional Neural Networks (CNNs) for this purpose, they aren't …
Node-link representation-based deep learning method for reconstructing trabecular bone from low-resolution images
H Koh, BJ Chun, IG Jang - Expert Systems with Applications, 2024 - Elsevier
The current gold standard for osteoporosis, areal bone mineral density (aBMD)
measurement, cannot precisely assess bone strength due to limitations in investigating …
measurement, cannot precisely assess bone strength due to limitations in investigating …
Research on multi-stage topology optimization method based on latent diffusion model
W Zhang, G Zhao, L Su - Advanced Engineering Informatics, 2025 - Elsevier
Topology optimization aims to identify the optimal material distribution to enhance structural
performance. Traditional methods such as Solid Isotropic Material with Penalization (SIMP) …
performance. Traditional methods such as Solid Isotropic Material with Penalization (SIMP) …
Generative Optimization: A Perspective on AI-Enhanced Problem Solving in Engineering
The field of engineering is shaped by the tools and methods used to solve problems.
Optimization is one such class of powerful, robust, and effective engineering tools proven …
Optimization is one such class of powerful, robust, and effective engineering tools proven …
Deep Learning in Deterministic Computational Mechanics
L Herrmann, S Kollmannsberger - arXiv preprint arXiv:2309.15421, 2023 - arxiv.org
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 …