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Jul 26, 2023 · Title:Learning to simulate partially known spatio-temporal dynamics with trainable difference operators ; Subjects: Machine Learning (cs.LG); ...
A new hybrid architecture named PDE-Net++ is proposed, which effectively combines difference operators and black-box models, essentially realizing the explicit ...
Apr 29, 2024 · Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention. However, most existing methods adopt ...
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators ... by Xiang Huang, et al. ... Recently, using neural networks to ...
May 28, 2024 · Learning to simulate partially known spatio-temporal dynamics with trainable difference operators. Xiang Huang, Zhuoyuan Li, Hongsheng Liu ...
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators ... simulate spatio-temporal dynamics has received a lot ...
Jan 13, 2024 · An attempt was made by Shi et al. to learn PDE-governed dynamics by limiting trainable parameters of CNN using finite difference operators.
Jul 26, 2023 · Learning to simulate partially known spatio-temporal dynamics with trainable difference operators.
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[5] Huang X, et al. Learning to simulate partially known spatio-temporal dynamics with trainable difference operators. arXiv 2023. [6] Li Z, Huang D Z, Liu ...
Feb 28, 2024 · Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress.