新疆大学数学与系统科学学院
纸质出版:2022
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[1]彭湃,冯新龙.基于两重网格的深度学习方法求解定常偏微分方程[J].新疆大学学报(自然科学版)(中英文),2022,39(04):412-420.
[1]彭湃,冯新龙.基于两重网格的深度学习方法求解定常偏微分方程[J].新疆大学学报(自然科学版)(中英文),2022,39(04):412-420. DOI: 10.13568/j.cnki.651094.651316.2021.09.22.0003.
DOI:10.13568/j.cnki.651094.651316.2021.09.22.0003.
随着机器学习在多个领域的研究取得进展,物理信息神经网络为偏微分方程的求解提供了新思路,但该方法难以获得高精度的数值解.结合物理信息神经网络与两重网格求解偏微分方程的思想,提出了基于两重网格的深度学习方法求解定常偏微分方程.针对神经网络求解多目标问题,采取了动态权重策略平衡损失函数中各项之间的数值差异,有效缓解了梯度病态现象.最后,给出了若干数值实验,验证了结合动态权重策略的深度学习方法在提高计算精度上的有效性.
With the progress of machine learning in many fields
physics-informed neural networks provide new ideas for solving partial differential equations
but this method is difficult to obtain high-precision numerical solutions. Absorbing the philosophy of physics-informed neural network and the two-grid solution of partial differential equations
this paper puts forward the deep learning method based on two-grid for solving stationary partial differential equations. For the neural network to solve the multi-objective problem
the dynamic weight strategy is adopted to balance the numerical difference between the items in the loss function
and alleviate the gradient ill-conditioned phenomenon. Finally
this paper gives several numerical experiments to verify the effectiveness of the deep learning method combined with dynamic weight strategy in improving the calculation accuracy.
KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenet classification with deep convolutional neural networks[J].Advances in Neural Information Processing Systems,2012,25:1097-1105.
LAKE B M,SALAKHUTDINOV R,TENENBAUM J B.Human-level concept learning through probabilistic program induction[J].Science,2015,350:1332-1338.
LECUN Y,BENGIO Y.Convolutional networks for images,speech,and time series[J].The Handbook of Brain Theory and Neural Networks,1995,3361(10):1995.
RAISSI M,PERDIKARIS P,KARNIADAKIS G E.Physics-informed neural networks:a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J].Journal of Computational Physics,2019,378:686-707.
TUKEZI A,ABUDUREXITI A.The study of a new numerical method for parabolic partial differential equations[J].Journal of Xinjiang University(Natural Science Edition),2014,31(1):64-69.
JIN X W,CAI S Z,LI H,et al.NSFnets(Navier-Stokes Flow nets):physics-informed neural networks for the incompressible Navier-Stokes equations[J].Joural of Computational Physics,2021,426:109951.
DWIVEDI V,PARASHAR N,SRINIVASAN B.Distributed physics informed neural network for data-efficient solution to partial differential equations[J].ar Xiv preprint ar Xiv,2019,https://doi.org/10.48550/ar Xiv.1907.08967.
KHARAZMI E,ZHANG Z Q,KARNIADAKIS G E.Variational physics-informed neural networks for solving partial differential equations[J].ar Xiv preprint ar Xiv,2019,https://doi.org/10.48550/ar Xiv.1912.00873.
WANG X J,JIA H E.A two-grid method with backtracking for the incompressible Brinkman-Forchheimer equations[J].Journal of Xinjiang University(Natural Science Edition in Chinese and English),2021,38(3):275-284.
BAYDIN A G,PEARLMUTTER B A,RADUL A A,et al.Automatic differentiation in machine learning:a survey[J].The Journal of Machine Learning Research,2018,18:1-43.
WANG S F,TENG Y J,PERDIKARIS P.Understanding and mitigating gradient pathologies in physics-informed neural networks[J].SIAM Journal on Science Computing,2021,43(5):3055-3081.
WU J L,GUI D W,LIU D M,et al.The characteristic variational multiscale method for time dependent conduction-convection problems[J].International Communications in Heat and Mass Transfer,2015,68:58-68.
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