报告人：徐振礼教授

报告题目：Sum-of-Gaussians Based Machine Learning Force Field and Tensor Neural Networks for High-Dimensional Equations

报告摘要：This talk includes two topics by using sum-of-Gaussians for constructing neural networks. The first topic is machine-learning interatomic potentials which have emerged as a revolutionary class of force-field models in molecular simulations. We propose a Sum-of-Gaussians Neural Network (SOG-Net) for integrating long-range interactions into machine learning force field. By learning sum-of-Gaussians multipliers across different convolution layers, the SOG-Net adaptively captures diverse long-range decay behaviors while maintaining close-to-linear computational complexity during training and simulation via non-uniform fast Fourier transforms. In the second topic, we introduce an accurate, efficient, and low-memory sum-of-Gaussians tensor neural network (SOG-TNN) algorithm for solving the high-dimensional Schrödinger equation. The Coulomb interaction is handled by an SOG decomposition such that it is dimensionally separable, leading to a tensor representation with rapid convergence. Range-splitting scheme is develop to partition the Gaussian terms into short-, long-, and mid-range components such that they can be approximated accurately. Numerical results demonstrate the outstanding performance of the new methods.

报告人简介： 徐振礼，上海交通大学特聘教授。中国科学技术大学本硕博，曾任美国北卡罗莱纳大学夏洛特分校博士后、德国斯图加特大学洪堡学者。2010年起任上海交通大学特别研究员、教授、长聘正教授、特聘教授，2019-2021年任数学学院副院长，2021年起任教务处副处长。2010年入选新世纪优秀人才计划，2012年中组部青年拔尖人才计划，2023年获国家自然科学基金杰出青年基金。兼任上海数学会副理事长、上海国家应用数学中心（上海交大分中心）副主任、中国高等教育学会理科教育专业委员会常务理事等职，以及Advances in Applied Mathematics and Mechanics、Communications in Mathematical Sciences和Mathematical and Computational Applications等杂志编委。研究方向为快速算法和高性能计算、分子动力学算法和偏微分方程的数值方法等，发表90多篇研究论文。

报告时间：2026年1月11号上午9:00-12:00

报告形式：线下：理学楼609