Physics-informed neural networks(PINNs)have shown considerable promise for performing numerical simulations in fluid mechanics.They provide mesh-free,end-to-end approaches by embedding physical laws into their loss fu...Physics-informed neural networks(PINNs)have shown considerable promise for performing numerical simulations in fluid mechanics.They provide mesh-free,end-to-end approaches by embedding physical laws into their loss functions.However,when addressing complex flow problems,PINNs still face some challenges such as activation saturation and vanishing gradients in deep network training,leading to slow convergence and insufficient prediction accuracy.We present physics-informed neural networks incorporating lattice Boltzmann method optimized by tanh robust weight initialization(T-PINN-LBM)to address these challenges.This approach fuses the mesoscopic lattice Boltzmann model with the automatic differentiation framework of PINNs.It also implements a tanh robust weight initialization method derived from fixed point analysis.This model effectively mitigates activation and gradient decay in deep networks,improving convergence speed and data efficiency in multiscale flow simulations.We validate the effectiveness of the model on the classical arithmetic example of lid-driven cavity flow.Compared to the traditional Xavier initialized PINN and PINN-LBM,T-PINNLBM reduces the mean absolute error(MAE)by one order of magnitude at the same network depth and maintains stable convergence in deeper networks.The results demonstrate that this model can accurately capture complex flow structures without prior data,providing a new feasible pathway for data-free driven fluid simulation.展开更多
We report the effect of UVoB irradiation （9.6 kJ m-2 day^-） on interspecific competition between two species of macroalgae, Ulva pertusa （U） and Grateloupiafilicina （G）, in co-culture. Growth of U. pertusa and G...We report the effect of UVoB irradiation （9.6 kJ m-2 day^-） on interspecific competition between two species of macroalgae, Ulva pertusa （U） and Grateloupiafilicina （G）, in co-culture. Growth of U. pertusa and G. filicina was inhibited by UV-B irradiation in mono-culture and specific growth rate （μ） declined as a result. Interspecific competition between U. pertusa and G filicina was closely related to the initial weights when co-cultured. When initial ratios of U. pertusa （U） to G filicina （G） were U：G=I.2：I and 1：1, U. pertusa was the dominant algae. When the initial U：G ratio was 1：1.2, G. filicina was competitively dominant in the earlier stage, but U. pertusa grew faster, superseding G. filicina in the later stage. At initial ration U：G = 1：1.4, G. filicina was predominant. Under UV-B irradiation, the competitive ability of G filicina was weakened and the interspecific competitive balance favored U. pertusa, which suggests that G. filicina was more sensitive to UV-B irradiation. We also probed the potential allelopathic effects between the two species, which led to mutual growth inhibition.展开更多
Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-mi...Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.展开更多
The positive energy theorem for weighted asymptotically flat spin manifolds was proved by Baldauf and Ozuch(2022)and for the non-spin case by Chu and Zhu(2024).In this paper,we generalize the positive energy theorem f...The positive energy theorem for weighted asymptotically flat spin manifolds was proved by Baldauf and Ozuch(2022)and for the non-spin case by Chu and Zhu(2024).In this paper,we generalize the positive energy theorem for 3-dimensional asymptotically anti-de Sitter initial data sets to weighted asymptotically antide Sitter initial data sets assuming that the weighted dominant energy condition holds.展开更多
Since of the scale and the various shapes of down in the image,it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy,even for ...Since of the scale and the various shapes of down in the image,it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy,even for the Traditional Convolutional Neural Network(TCNN).To deal with the above problems,a Deep Convolutional Neural Network(DCNN)for down image classification is constructed,and a new weight initialization method is proposed.Firstly,the salient regions of a down image were cut from the image using the visual saliency model.Then,these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters,which accord with the statistical characteristics of dataset.At last,a DCNN with Inception module and its variants was constructed.To improve the recognition accuracy,the depth of the network is deepened.The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN,when recognizing the down in the images.The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN.展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.12474453,12174085,and 12404530).
文摘Physics-informed neural networks(PINNs)have shown considerable promise for performing numerical simulations in fluid mechanics.They provide mesh-free,end-to-end approaches by embedding physical laws into their loss functions.However,when addressing complex flow problems,PINNs still face some challenges such as activation saturation and vanishing gradients in deep network training,leading to slow convergence and insufficient prediction accuracy.We present physics-informed neural networks incorporating lattice Boltzmann method optimized by tanh robust weight initialization(T-PINN-LBM)to address these challenges.This approach fuses the mesoscopic lattice Boltzmann model with the automatic differentiation framework of PINNs.It also implements a tanh robust weight initialization method derived from fixed point analysis.This model effectively mitigates activation and gradient decay in deep networks,improving convergence speed and data efficiency in multiscale flow simulations.We validate the effectiveness of the model on the classical arithmetic example of lid-driven cavity flow.Compared to the traditional Xavier initialized PINN and PINN-LBM,T-PINNLBM reduces the mean absolute error(MAE)by one order of magnitude at the same network depth and maintains stable convergence in deeper networks.The results demonstrate that this model can accurately capture complex flow structures without prior data,providing a new feasible pathway for data-free driven fluid simulation.
基金Supported by the National Natural Science Foundation of China(No.30270258)the Natural Science Foundation of Shandong Province(No.2007ZRB01903)
文摘We report the effect of UVoB irradiation （9.6 kJ m-2 day^-） on interspecific competition between two species of macroalgae, Ulva pertusa （U） and Grateloupiafilicina （G）, in co-culture. Growth of U. pertusa and G. filicina was inhibited by UV-B irradiation in mono-culture and specific growth rate （μ） declined as a result. Interspecific competition between U. pertusa and G filicina was closely related to the initial weights when co-cultured. When initial ratios of U. pertusa （U） to G filicina （G） were U：G=I.2：I and 1：1, U. pertusa was the dominant algae. When the initial U：G ratio was 1：1.2, G. filicina was competitively dominant in the earlier stage, but U. pertusa grew faster, superseding G. filicina in the later stage. At initial ration U：G = 1：1.4, G. filicina was predominant. Under UV-B irradiation, the competitive ability of G filicina was weakened and the interspecific competitive balance favored U. pertusa, which suggests that G. filicina was more sensitive to UV-B irradiation. We also probed the potential allelopathic effects between the two species, which led to mutual growth inhibition.
基金financially supported by the Key Project of National Natural Science Foundation of China (No. 41930431)the Project of National Natural Science Foundation of China (Nos. 41904121, 41804133, and 41974116)Joint Guidance Project of Natural Science Foundation of Heilongjiang Province (No. LH2020D006)
文摘Based on the CNN-LSTM fusion deep neural network,this paper proposes a seismic velocity model building method that can simultaneously estimate the root mean square(RMS)velocity and interval velocity from the common-midpoint(CMP)gather.In the proposed method,a convolutional neural network(CNN)Encoder and two long short-term memory networks(LSTMs)are used to extract spatial and temporal features from seismic signals,respectively,and a CNN Decoder is used to recover RMS velocity and interval velocity of underground media from various feature vectors.To address the problems of unstable gradients and easily fall into a local minimum in the deep neural network training process,we propose to use Kaiming normal initialization with zero negative slopes of rectifi ed units and to adjust the network learning process by optimizing the mean square error(MSE)loss function with the introduction of a freezing factor.The experiments on testing dataset show that CNN-LSTM fusion deep neural network can predict RMS velocity as well as interval velocity more accurately,and its inversion accuracy is superior to that of single neural network models.The predictions on the complex structures and Marmousi model are consistent with the true velocity variation trends,and the predictions on fi eld data can eff ectively correct the phase axis,improve the lateral continuity of phase axis and quality of stack section,indicating the eff ectiveness and decent generalization capability of the proposed method.
基金supported by National Natural Science Foundation of China(Grant No.12326602)the special foundation for Guangxi Ba Gui Scholars and Junwu Scholars。
文摘The positive energy theorem for weighted asymptotically flat spin manifolds was proved by Baldauf and Ozuch(2022)and for the non-spin case by Chu and Zhu(2024).In this paper,we generalize the positive energy theorem for 3-dimensional asymptotically anti-de Sitter initial data sets to weighted asymptotically antide Sitter initial data sets assuming that the weighted dominant energy condition holds.
基金supported by the Natural Science Foundation of Hebei Provence[grant numbers:F2015201033,F2017201069]the foundation of H3C[grant number:2017A20004]。
文摘Since of the scale and the various shapes of down in the image,it is difficult for traditional image recognition method to correctly recognize the type of down image and get the required recognition accuracy,even for the Traditional Convolutional Neural Network(TCNN).To deal with the above problems,a Deep Convolutional Neural Network(DCNN)for down image classification is constructed,and a new weight initialization method is proposed.Firstly,the salient regions of a down image were cut from the image using the visual saliency model.Then,these salient regions of the image were used to train a sparse autoencoder and get a collection of convolutional filters,which accord with the statistical characteristics of dataset.At last,a DCNN with Inception module and its variants was constructed.To improve the recognition accuracy,the depth of the network is deepened.The experiment results indicate that the constructed DCNN increases the recognition accuracy by 2.7% compared to TCNN,when recognizing the down in the images.The convergence rate of the proposed DCNN with the new weight initialization method is improved by 25.5% compared to TCNN.
