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Strongly heterogeneous material properties, which translate to the heterogeneity of coefficients of the PDEs and discontinuous features in the solutions, require specialized techniques for the forward ... This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator ... S3 Conditional generative adversarial network We propose a data-driven model order reduction for physics-based problems using conditional generative adversarial network (cGAN)    . ...arXiv:2105.13136v1 fatcat:4nyoeny7jregldwizncu3spxhy
A series of ML algorithms with different levels of depth and supervision are trained using a data-driven approach. ... A modified GANs with lower network depth showed good performance in the generation of failure probability maps, with good reproduction of the non-deterministic micro-scale response. ... The main novelty is the use of a data-driven ML approach instead of a parametric random field for the reproduction of material heterogeneity. ...doi:10.3390/ma15030965 pmid:35160911 pmcid:PMC8838419 fatcat:c47on336lzc6lgv4ibeykyuyoy
A review of the archaeological occurrences illustrates that six sites, including Bonnell, are located within or nearby pine forest habitats suitable for Thick-billed Parrot ecology. ... This biogeographic distribution, combined with evidence from extant ecology, historic occurrences, and reintroductions, demonstrates an association between the archaeological sites and viable Thick-bill ... We propose an algorithm for seismic data interpolation using generative adversarial networks (GANs), which are a type of deep neural networks. ...doi:10.26153/tsw/7090 fatcat:sledfarqbzbt3fitvtdpl4fj7y