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作 者:兰鹏 李海潮[1] 叶新宇[1] 张升[1] 盛岱超 LAN Peng;LI Hai-chao;YE Xin-yu;ZHANG Sheng;SHENG Dai-chao(School of Civil Engineering,Changsha Central South University,Changsha 400041,China;School of Civil and Environmental Engineering,Sydney University of Technology,Sydney,NSW 2007,Australia)
机构地区:[1]中南大学土木工程学院,湖南长沙400041 [2]悉尼科技大学土木与环境工程学院,悉尼NSW2007
出 处:《岩土工程学报》2021年第3期586-592,F0002,F0003,共9页Chinese Journal of Geotechnical Engineering
基 金:国家自然科学基金优秀青年基金项目(51722812);湖湘高层次人才聚集工程项目(2018RS3016);湖南省研究生科研创新项目(CX20200220);中南大学研究生自主探索创新项目(2020zzts613)。
摘 要:物理信息神经网络(PINNs)算法采用自动微分方法将偏微分方程直接嵌入神经网络中,从而实现对偏微分方程的智能求解,属于一种新型的无网格算法,具有收敛速度快和计算精度高等优点。PINNs不仅能够实现对偏微分方程求解,还能够对偏微分方程未知参数进行反演,因此对岩土工程复杂问题具有广泛的应用前景。为了验证PINNs算法在岩土工程领域的可行性,对连续排水边界条件下的一维固结理论进行求解和界面参数反演。计算结果表明,PINNs数值结果与解析解具有高度一致性,且界面参数反演结果准确,说明PINNs算法能够为岩土工程相关问题提供新的求解思路。The physical information neural networks(PINNs) algorithm, a new mesh-free algorithm, uses the automatic differential method to embed the partial differential equation directly into the neural networks so as to realize the intelligent solution of the partial differential equation, which has the advantages of fast convergence speed and high computational accuracy. The PINNs algorithm has a promising application in geotechnical engineering because it can solve the complex partial differential equations(PDEs) and inverse the unknown parameters of the PDEs. In order to verify the feasibility of the PINNs algorithm in geotechnical engineering, the one-dimensional consolidation process with the continuous drainage boundary condition is taken as an example to illustrate the procedures of the PINNs algorithm in terms of both the forward and inverse problems. The results show that the PINNs solution is highly consistent with the analytical one, indicating that the PINNs algorithm can provide an alternative approach for solving the related problems in geotechnical engineering.
关 键 词:物理信息神经网络(PINNs) 自动微分 无网格算法 参数反演 连续排水边界条件
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