Deep generative models in engineering design: A review
L Regenwetter, AH Nobari… - Journal of …, 2022 - asmedigitalcollection.asme.org
Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …
design process and improve access to highly optimized and customized products across …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …
essential layer of safety assurance that could lead to more principled decision making by …
Topologygan: Topology optimization using generative adversarial networks based on physical fields over the initial domain
In topology optimization using deep learning, the load and boundary conditions represented
as vectors or sparse matrices often miss the opportunity to encode a rich view of the design …
as vectors or sparse matrices often miss the opportunity to encode a rich view of the design …
A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment
M Elahi, SO Afolaranmi, JL Martinez Lastra… - Discover Artificial …, 2023 - Springer
Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial
intelligence (AI) has empowered smart manufacturing and digital transformation. AI …
intelligence (AI) has empowered smart manufacturing and digital transformation. AI …
Data-driven generative design for mass customization: A case study
Generative design provides a promising algorithmic solution for mass customization of
products, improving both product variety and design efficiency. However, the current …
products, improving both product variety and design efficiency. However, the current …
GAN-based generation of realistic 3D volumetric data: A systematic review and taxonomy
With the massive proliferation of data-driven algorithms, such as deep learning-based
approaches, the availability of high-quality data is of great interest. Volumetric data is very …
approaches, the availability of high-quality data is of great interest. Volumetric data is very …
[HTML][HTML] Data-driven multifidelity topology design using a deep generative model: Application to forced convection heat transfer problems
K Yaji, S Yamasaki, K Fujita - Computer Methods in Applied Mechanics and …, 2022 - Elsevier
Topology optimization is a powerful methodology for generating novel designs with a high
degree of design freedom. In exchange for this attractive feature, topology optimization …
degree of design freedom. In exchange for this attractive feature, topology optimization …
Conceptual design generation using large language models
Abstract Concept generation is a creative step in the conceptual design phase, where
designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to …
designers often turn to brainstorming, mindmapping, or crowdsourcing design ideas to …
The evolution and impact of human confidence in artificial intelligence and in themselves on AI-assisted decision-making in design
Decision-making assistance by artificial intelligence (AI) during design is only effective when
human designers properly utilize the AI input. However, designers often misjudge the AI's …
human designers properly utilize the AI input. However, designers often misjudge the AI's …
Advancing 3D bioprinting through machine learning and artificial intelligence
Abstract 3D bioprinting, a vital tool in tissue engineering, drug testing, and disease
modeling, is increasingly integrated with machine learning (ML) and artificial intelligence …
modeling, is increasingly integrated with machine learning (ML) and artificial intelligence …