Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Shu, Dule"'
Autor:
Shu, Dule, Farimani, Amir Barati
The success of diffusion probabilistic models in generative tasks, such as text-to-image generation, has motivated the exploration of their application to regression problems commonly encountered in scientific computing and various other domains. In
Externí odkaz:
http://arxiv.org/abs/2408.04718
Autor:
Li, Zijie, Patil, Saurabh, Ogoke, Francis, Shu, Dule, Zhen, Wilson, Schneier, Michael, Buchanan, Jr., John R., Farimani, Amir Barati
Neural networks have shown promising potential in accelerating the numerical simulation of systems governed by partial differential equations (PDEs). Different from many existing neural network surrogates operating on high-dimensional discretized fie
Externí odkaz:
http://arxiv.org/abs/2402.17853
Fluid data completion is a research problem with high potential benefit for both experimental and computational fluid dynamics. An effective fluid data completion method reduces the required number of sensors in a fluid dynamics experiment, and allow
Externí odkaz:
http://arxiv.org/abs/2402.17185
Transformer has shown state-of-the-art performance on various applications and has recently emerged as a promising tool for surrogate modeling of partial differential equations (PDEs). Despite the introduction of linear-complexity attention, applying
Externí odkaz:
http://arxiv.org/abs/2305.17560
Machine learning models are gaining increasing popularity in the domain of fluid dynamics for their potential to accelerate the production of high-fidelity computational fluid dynamics data. However, many recently proposed machine learning models for
Externí odkaz:
http://arxiv.org/abs/2211.14680
Publikováno v:
In Journal of Computational Physics 1 April 2023 478
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Akademický článek
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Publikováno v:
Design Science; 5/12/2020, Vol. 6, p1-33, 33p