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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]
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 ...
This software is primarily used by architects and engineers in the design of buildings, but has also been used to model natural and biological structures and mathematical systems. Generative Components currently runs exclusively on Microsoft Windows operating systems, and in English. Bentley Systems Incorporated offers GC as a free download. [6]
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.
a generative model is a model of the conditional probability of the observable X, given a target y, symbolically, (=) [2] a discriminative model is a model of the conditional probability of the target Y , given an observation x , symbolically, P ( Y ∣ X = x ) {\displaystyle P(Y\mid X=x)} [ 3 ]
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]
A large collection of Question to SPARQL specially design for Open Domain Neural Question Answering over DBpedia Knowledgebase. This dataset contains a large collection of Open Neural SPARQL Templates and instances for training Neural SPARQL Machines; it was pre-processed by semi-automatic annotation tools as well as by three SPARQL experts.