题    目：Convection-Diffusion Equation: An axiomatized Framework for Neural Networks

主讲人：史作强 教授

单    位：清华大学

时    间：2024年12月6日 15:00

地    点：郑州校区九章学堂南楼C座302

摘    要：Bridging neural networks with partial differential equations holds significant importance, as it not only enhances the interpretability of neural networks but also sheds light on designing network architectures. In this talk, we establish convection-diffusion equation models based on rigorous theoretical analysis. The convection-diffusion equation model not only covers existing network structures, but also illuminates novel network design, COnvection dIffusion Networks (COIN). Numerical results demonstrate the effecliveness of COIN in various benchmarks, as well as its potential in novel tasks such as disease prediction.

简    介：史作强，清华大学丘成桐数学科学中心长聘教授，北京雁栖湖应用数学研究院双聘研究员，主要研究方向为偏微分方程数值方法，图像处理和机器学习中的微分方程模型，非线性非平稳信号时频分析等，在ACHA，SIAM系列期刊，Advances in Mathematics，ARMA等国际知名学术期刊发表文章70余篇。