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Jan, 2024
RiemannONets:用于Riemann问题的可解释神经操作器
RiemannONets: Interpretable Neural Operators for Riemann Problems
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Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap, George Em Karniadakis
TL;DR
使用神经算子对压力跳变(高达$10^{10}$)的可压缩流中遇到的Riemann问题进行求解,通过简单的修改DeepONet使其在精确性、效率和稳健性方面取得显著效果,从而实现对实时预测Riemann问题的非常精确的解决方案。
Abstract
Developing the proper representations for
simulating
high-speed flows
with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ
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