Neural-network-based solver for a few soft matter problems（东吴物理大讲坛）

报告题目：Neural-network-based solver for a few soft matter problems

报告人：Jeff Z. Y. Chen, Professor, University of Waterloo

报告时间：2023年9月5日（周二）下午 14:00

报告地点：博习楼327会议室

报告摘要：

Many soft matter theoretical problems can be reformulated into minimizing a cost function, in which the field-based physical properties are adjusted to achieve the minimum. The Neural-network approach approximates these fields by forward-feeding neural networks and the machine-learning techniques adjust the network parameters to produce the approximation to the desirable solutions. The physical properties, such as the free energy, together with boundary conditions, etc., are modelled in the cost function. The decoupling between the function approximator and sampling space allows for further incorporation of the weighted Monte Carlo method. The algorithm is demonstrated here by solving a few classical theoretical problems in soft matter.

报告人简介：

陈征宇教授毕业于复旦大学物理系，1982年经CUSPEA计划赴美，于1988年获马里兰大学物理博士。陈教授现为加拿大滑铁卢大学天文与物理系教授。主要研究领域为统计物理和计算物理在软物质系统中的应用，曾创造性地解决高分子物理、液晶物理等领域中的理论问题。