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Solving Schrodinger Equation with Soft Constrained Monotonic Neural Network
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作者 LIU Xuan LI Hanlin +1 位作者 pu kaifang PANG Longgang 《原子核物理评论》 CAS CSCD 北大核心 2024年第1期379-384,共6页
Artificial Neural Network(ANN)has become a powerful tool in the field of scientific research with its powerful information encapsulation ability and convenient variational optimization method.In particular,there have ... Artificial Neural Network(ANN)has become a powerful tool in the field of scientific research with its powerful information encapsulation ability and convenient variational optimization method.In particular,there have been many recent advances in computational physics to solve variational problems.Deep Neural Network(DNN)is used to represent the wave function to solve quantum many-body problems using variational optimization.In this work we used a new Physics-Informed Neural Network(PINN)to represent the Cumulative Distribution Function(CDF)of some classical problems in quantum mechanics and to obtain their ground state wave function and ground state energy through the CDF.By benchmarking against the exact solution,the error of the results can be controlled at a very low level.This new network architecture and optimization method can provide a new choice for solving quantum many-body problems. 展开更多
关键词 deep neural network variational problem Cumulative distribution function ground state wave function
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