Mitigating Gradient Pathology in PINNs through Aligned Constraint
通过对齐约束缓解PINN中的梯度病理
机构 * Department of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden(信息科学与工程系,皇家理工学院,斯德哥尔摩,瑞典) ; School of Advanced Manufacturing and Robotics, Peking University, Beijing, China(先进制造与机器人学院,北京大学,北京,中国) ; School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou, China(先进技术学院,西安交通大学利物浦大学,苏州,中国) ; Department of AI, School of Engineering, Westlake University, Hangzhou, China(人工智能系,工程学院,西湖大学,杭州,中国)
专题命中 病理影像 :pathology(title,abstract);分类 cs.LG
AI总结 针对物理信息神经网络训练中梯度冲突导致的局部最优问题,提出约束对齐损失与流形提升方法,通过重新表述零阶项为对齐约束并引入延迟因子,显著提升数值稳定性和效率。
Comments Accepted by ICML 2026
Journal ref Forty-Third International Conference on Machine Learning (ICML 2026)