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Generative design, one of the four key methods for lightweight design in AM, is commonly applied to optimize structures for specific performance requirements. [25] Generative design can help create optimized solutions that balance multiple objectives, such as enhancing performance while minimizing cost. [26]
Download QR code; Print/export Download as PDF; Printable version; In other projects ... Questions to locate the appropriate venue(s) This page was last ...
Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
In machine learning, diffusion models, also known as diffusion probabilistic models or score-based generative models, are a class of latent variable generative models. A diffusion model consists of three major components: the forward process, the reverse process, and the sampling procedure. [ 1 ]
A generative model is a statistical model of the joint probability distribution (,) on a given observable variable X and target variable Y; [1] A generative model can be used to "generate" random instances of an observation x.
The term "generative 3D modelling" describes a different paradigm for describing shape. The main idea is to replace 3D objects by object-generating operations: A shape is described by a sequence of processing steps, rather than the triangles which are the result of applying these operations. Shape design becomes rule design.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
GANs are implicit generative models, [8] which means that they do not explicitly model the likelihood function nor provide a means for finding the latent variable corresponding to a given sample, unlike alternatives such as flow-based generative model. Main types of deep generative models that perform maximum likelihood estimation [9]