学术报告20260606：Hard constraint learning approaches with trainable influence functions for evolutionary equations

报告人：苏帅 副教授 北京工业大学

报告题目：Hard constraint learning approaches with trainable influence functions for evolutionary equations

摘 要：This talk introduces a novel deep learning approach for solving evolutionary equations, which integrates sequential learning strategies with an enhanced hard constraint strategy featuring trainable parameters, addressing the low computational accuracy of standard Physics-informed neural networks (PINNs) in large temporal domains. Sequential learning strategies divide a large temporal domain into multiple subintervals and solve them one by one in a chronological order, which naturally respects the principle of causality and improves the stability of the PINN solution. The improved hard constraint strategy strictly ensures the continuity and smoothness of the PINN solution at time interval nodes, and at the same time passes the information from the previous interval to the next interval, which avoids the incorrect/trivial solution at the position far from the initial time. Furthermore, by investigating the requirements of different types of equations on hard constraints, we design a novel influence function with trainable parameters for hard constraints, which provides theoretical and technical support for the effective implementations of hard constraint strategies, and significantly improves the universality and computational accuracy of our method. In addition, an adaptive time-domain partitioning algorithm is proposed, which plays an important role in the application of the proposed method as well as in the improvement of computational efficiency and accuracy.

报告人简介：苏帅，北京工业大学副教授，校聘教授，博士生导师。博士毕业于中国工程物理研究院北京应用物理与计算数学研究所，2019年-2021年于北京大学从事博士后研究。主要研究方向为偏微分方向数值解与计算流体力学。主持国家自然科学基金青年基金、计算物理全国重点实验室基金等，在Comput. Methods Appl. Mech. Engrg.，J. Comput. Phys.，Eng. Appl. Artif. Intel.等期刊发表SCI论文近二十篇。

报告时间：2026年6月6日，8:30--10:00

报告地点：数学科学学院512会议室

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2026年6月2日