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Learning PDEs/SPDEs via neural networks based on regularity structure

发布日期:2024-12-17点击数:

报告人:陈炳光 讲师(福建师范大学)

时间:2024年12月20日 10:00-

地点:600cc全讯白菜LD402


摘要:Artificial intelligence has brought technological innovation to various fields and achieved remarkable results, especially in modeling dynamic systems. In this talk, basic knowledge about deep learning will be presented.  We propose neural operator networks based on regularity structure theory to model PDEs/SPDEs. The key step of our network structure is to project the stochastic noise and initial values to the model feature vectors. The experimental results show that our networks significantly improve the accuracy of solutions and speed up inference time. Based on joint work with Qi Meng, Shiqi Gong, Peiyan Hu, etc.


邀请人:杨寰宇


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