为在稀疏测点超孔隙水压力数据条件下预测饱和软土的固结行为,引入物理信息深度算子网络(physics-informed deep operator network,PI-DeepONet)方法,并利用稀疏孔隙水压力测点数据对饱和土体全域内超孔隙水压力分布进行实时预测。通过...为在稀疏测点超孔隙水压力数据条件下预测饱和软土的固结行为,引入物理信息深度算子网络(physics-informed deep operator network,PI-DeepONet)方法,并利用稀疏孔隙水压力测点数据对饱和土体全域内超孔隙水压力分布进行实时预测。通过分析常规黏土变形固结及软弱黏土大变形固结2个实例进行预测,引入相对L2误差和R2这2个评估指标,验证了PI-DeepONet算法在预测全域超孔隙水压力演化方面的性能,并与纯数据驱动的DeepONet算法的计算结果进行了对比。预测结果表明:在相同的测点数目和各测点拥有相同超孔隙水压力数据量的条件下,DeepONet算法对全域超孔隙水压力的预测绝对误差在10^(-2)~10^(-1)左右,而PI-DeepONet算法的绝对误差范围则在10^(−3)~10^(-2)左右,表现出更好的预测效果。其次,在常规黏土变形固结行为研究中,通过对超孔隙水压力数据添加3种不同噪声水平来模拟现场监测环境,观察到即使噪声水平达到5%,PI-DeepONet算法仍能在水压力数据稀疏且带噪声的条件下提供高质量的全域超孔隙水压力实时预测。最后,在软弱黏土大变形固结行为研究中,将PI-DeepONet算法运用于上下边界排水速率不同的固结问题中,发现训练好的一维模型在单一测点条件下,能对其他界面参数条件下饱和土体全域内超孔隙水压力分布规律进行准确预测,表明PIDeepONet算法能为岩土工程中相关问题提供新的解决办法。展开更多
Reactive transport equations in porous media are critical in various scientific and engineering disciplines,but solving these equations can be computationally expensive when exploring different scenarios,such as varyi...Reactive transport equations in porous media are critical in various scientific and engineering disciplines,but solving these equations can be computationally expensive when exploring different scenarios,such as varying porous structures and initial or boundary conditions.The deep operator network(DeepONet)has emerged as a popular deep learning framework for solving parametric partial differential equations.However,applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures.To address this issue,we propose the Porous-DeepONet,a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks(CNNs)to learn the solution operators of parametric reactive transport equations in porous media.By incorporating CNNs,we can effectively capture the intricate features of porous media,enabling accurate and efficient learning of the solution operators.We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions,multiple phases,and multiphysical fields through five examples.This approach offers significant computational savings,potentially reducing the computation time by 50–1000 times compared with the finite-element method.Our work may provide a robust alternative for solving parametric reactive transport equations in porous media,paving the way for exploring complex phenomena in porous media.展开更多
Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated w...Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.展开更多
Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational s...Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.展开更多
Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand;this coordination depends on training and deploying control policies and optimiz...Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand;this coordination depends on training and deploying control policies and optimization routines,which in turn require predictive models that can be queried efficiently over large building clusters.However,conventional physics-based simulators are computationally prohibitive for large-scale control training,and simple data-driven surrogates often lack the generalization needed for heterogeneous clusters.This paper introduces ScaleONet,a deep operator network framework designed for scalable,control-oriented modeling of building-cluster thermal dynamics.ScaleONet leverages the DeepONet paradigm to decouple and share learning across buildings:an LSTM-based branch network encodes outdoor climate and individual HVAC control signals,while a multilayer perceptron(MLP)-based trunk network embeds prediction timestamps,enabling fast predictions for growing clusters with negligible extra cost for each additional building or timestep.To the authors’knowledge,this is the first operator-learning method tailored to indoor air temperature forecasting in heterogeneous building clusters.Validation on thirty Belgian buildings(GenkNet)simulated in Dymola shows that,although a non-operator-learning LSTM baseline slightly outperforms ScaleONet for single-building cases,its error grows monotonically with cluster size.In contrast,ScaleONet’s median per-building-per-day RMSE decreases from 0.59°C at three buildings to 0.53°C at ten and 0.47°C at thirty,compared to 0.95°C for the LSTM at thirty buildings-a 51%reduction in prediction error.Error analysis across envelope heat-loss coefficients(UAbuilding)further reveals that while the LSTM’s RMSE increases for high-𝑈𝐴structures,ScaleONet maintains uniformly low error.With millisecond-scale inference(approximately 4 ms per sample for thirty buildings),ScaleONet is well suited for large-scale reinforcement learning,receding-horizon optimization,and real-time model predictive control.展开更多
Visual tracking is a classical computer vision problem with many applications.Efficient convolution operators(ECO)is one of the most outstanding visual tracking algorithms in recent years,it has shown great performanc...Visual tracking is a classical computer vision problem with many applications.Efficient convolution operators(ECO)is one of the most outstanding visual tracking algorithms in recent years,it has shown great performance using discriminative correlation filter(DCF)together with HOG,color maps and VGGNet features.Inspired by new deep learning models,this paper propose a hybrid efficient convolution operators integrating fully convolution network(FCN)and residual network(ResNet)for visual tracking,where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects,respectively.Compared with the traditional VGGNet,our approach has higher accuracy for dealing with the issues of segmentation and image size.The experiments show that our approach would obtain better performance than ECO in terms of precision plot and success rate plot on OTB-2013 and UAV123 datasets.展开更多
Power flow adjustment is a sequential decision problem.The operator makes decisions to ensure that the power flow meets the system's operational constraints,thereby obtaining a typical operating mode power flow.Ho...Power flow adjustment is a sequential decision problem.The operator makes decisions to ensure that the power flow meets the system's operational constraints,thereby obtaining a typical operating mode power flow.However,this decision-making method relies heavily on human experience,which is inefficient when the system is complex.In addition,the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment.In order to improve the efficiency and intelligence of power flow adjustment,this paper proposes a power flow adjustment method based on deep reinforcement learning.Combining deep reinforcement learning theory with traditional power system operation mode analysis,the concept of region mapping is proposed to describe the adjustment process,so as to analyze the process of power flow calculation and manual adjustment.Considering the characteristics of power flow adjustment,a Markov decision process model suitable for power flow adjustment is constructed.On this basis,a double Q network learning method suitable for power flow adjustment is proposed.This method can adjust the power flow according to the set adjustment route,thus improving the intelligent level of power flow adjustment.The method in this paper is tested on China Electric Power Research Institute(CEPRI)test system.展开更多
In this paper,we propose a DeepONet structure with causality to represent causal linear operators between Banach spaces of time-dependent signals.The theorem of universal approximations to nonlinear operators proposed...In this paper,we propose a DeepONet structure with causality to represent causal linear operators between Banach spaces of time-dependent signals.The theorem of universal approximations to nonlinear operators proposed in[5]is extended to operators with causalities,and the proposed Causality-DeepONet implements the physical causality in its framework.The proposed Causality-DeepONet considers causality(the state of the system at the current time is not affected by that of the future,but only by its current state and past history)and uses a convolution-type weight in its design.To demonstrate its effectiveness in handling the causal response of a physical system,the Causality-DeepONet is applied to learn the operator representing the response of a building due to earthquake ground accelerations.Extensive numerical tests and comparisons with some existing variants of DeepONet are carried out,and the Causality-DeepONet clearly shows its unique capability to learn the retarded dynamic responses of the seismic response operator with good accuracy.展开更多
文摘为在稀疏测点超孔隙水压力数据条件下预测饱和软土的固结行为,引入物理信息深度算子网络(physics-informed deep operator network,PI-DeepONet)方法,并利用稀疏孔隙水压力测点数据对饱和土体全域内超孔隙水压力分布进行实时预测。通过分析常规黏土变形固结及软弱黏土大变形固结2个实例进行预测,引入相对L2误差和R2这2个评估指标,验证了PI-DeepONet算法在预测全域超孔隙水压力演化方面的性能,并与纯数据驱动的DeepONet算法的计算结果进行了对比。预测结果表明:在相同的测点数目和各测点拥有相同超孔隙水压力数据量的条件下,DeepONet算法对全域超孔隙水压力的预测绝对误差在10^(-2)~10^(-1)左右,而PI-DeepONet算法的绝对误差范围则在10^(−3)~10^(-2)左右,表现出更好的预测效果。其次,在常规黏土变形固结行为研究中,通过对超孔隙水压力数据添加3种不同噪声水平来模拟现场监测环境,观察到即使噪声水平达到5%,PI-DeepONet算法仍能在水压力数据稀疏且带噪声的条件下提供高质量的全域超孔隙水压力实时预测。最后,在软弱黏土大变形固结行为研究中,将PI-DeepONet算法运用于上下边界排水速率不同的固结问题中,发现训练好的一维模型在单一测点条件下,能对其他界面参数条件下饱和土体全域内超孔隙水压力分布规律进行准确预测,表明PIDeepONet算法能为岩土工程中相关问题提供新的解决办法。
基金supported by the National Key Research and Development Program of China(2022YFA1503501)the National Natural Science Foundation of China(22378112,22278127,and 22078088)+1 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Shanghai Rising-Star Program(21QA1401900).
文摘Reactive transport equations in porous media are critical in various scientific and engineering disciplines,but solving these equations can be computationally expensive when exploring different scenarios,such as varying porous structures and initial or boundary conditions.The deep operator network(DeepONet)has emerged as a popular deep learning framework for solving parametric partial differential equations.However,applying the DeepONet to porous media presents significant challenges due to its limited capability to extract representative features from intricate structures.To address this issue,we propose the Porous-DeepONet,a simple yet highly effective extension of the DeepONet framework that leverages convolutional neural networks(CNNs)to learn the solution operators of parametric reactive transport equations in porous media.By incorporating CNNs,we can effectively capture the intricate features of porous media,enabling accurate and efficient learning of the solution operators.We demonstrate the effectiveness of the Porous-DeepONet in accurately and rapidly learning the solution operators of parametric reactive transport equations with various boundary conditions,multiple phases,and multiphysical fields through five examples.This approach offers significant computational savings,potentially reducing the computation time by 50–1000 times compared with the finite-element method.Our work may provide a robust alternative for solving parametric reactive transport equations in porous media,paving the way for exploring complex phenomena in porous media.
基金funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED)under Grant No.105.08-2019.03.
文摘Flash floods are one of the most dangerous natural disasters,especially in hilly terrain,causing loss of life,property,and infrastructures and sudden disruption of traffic.These types of floods are mostly associated with landslides and erosion of roads within a short time.Most of Vietnamis hilly and mountainous;thus,the problem due to flash flood is severe and requires systematic studies to correctly identify flood susceptible areas for proper landuse planning and traffic management.In this study,three Machine Learning(ML)methods namely Deep Learning Neural Network(DL),Correlation-based FeatureWeighted Naive Bayes(CFWNB),and Adaboost(AB-CFWNB)were used for the development of flash flood susceptibility maps for hilly road section(115 km length)of National Highway(NH)-6 inHoa Binh province,Vietnam.In the proposedmodels,88 past flash flood events were used together with 14 flash floods affecting topographical and geo-environmental factors.The performance of themodels was evaluated using standard statisticalmeasures including Receiver Operating Characteristic(ROC)Curve,Area Under Curve(AUC)and Root Mean Square Error(RMSE).The results revealed that all the models performed well(AUC>0.80)in predicting flash flood susceptibility zones,but the performance of the DL model is the best(AUC:0.972,RMSE:0.352).Therefore,the DL model can be applied to develop an accurate flash flood susceptibility map of hilly terrain which can be used for proper planning and designing of the highways and other infrastructure facilities besides landuse management of the area.
基金The Key R&D Project of Jilin Province,Grant/Award Number:20230201067GX。
文摘Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system.A novel deep time series aggregation scheme(DTSAs)is proposed to generate typical operational scenarios,considering the large amount of historical operational snapshot data.Specifically,DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios.A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into highdimensional spaces.This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models.The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots.Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods.In addition,experiments with different new energy access ratios were conducted to verify the robustness of the proposed method.DTSAs enable dispatchers to master the operation experience of the power system in advance,and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.
基金supported by KU Leuven,Belgium through the TECHPED-C2 project(C24M/21/021)which investigates tech-nically feasible and effective solutions for Positive Energy Districts.Additional support was provided by the National University of Singa-pore,Singapore through the Start-Up Grant(A-0009876-00-00)the Ministry of Education,Singapore,under the Academic Research Fund Tier 1(A-8003235-00-00).
文摘Delivering energy flexibility at the district scale entails coordinating control actions across many buildings to shape aggregate demand;this coordination depends on training and deploying control policies and optimization routines,which in turn require predictive models that can be queried efficiently over large building clusters.However,conventional physics-based simulators are computationally prohibitive for large-scale control training,and simple data-driven surrogates often lack the generalization needed for heterogeneous clusters.This paper introduces ScaleONet,a deep operator network framework designed for scalable,control-oriented modeling of building-cluster thermal dynamics.ScaleONet leverages the DeepONet paradigm to decouple and share learning across buildings:an LSTM-based branch network encodes outdoor climate and individual HVAC control signals,while a multilayer perceptron(MLP)-based trunk network embeds prediction timestamps,enabling fast predictions for growing clusters with negligible extra cost for each additional building or timestep.To the authors’knowledge,this is the first operator-learning method tailored to indoor air temperature forecasting in heterogeneous building clusters.Validation on thirty Belgian buildings(GenkNet)simulated in Dymola shows that,although a non-operator-learning LSTM baseline slightly outperforms ScaleONet for single-building cases,its error grows monotonically with cluster size.In contrast,ScaleONet’s median per-building-per-day RMSE decreases from 0.59°C at three buildings to 0.53°C at ten and 0.47°C at thirty,compared to 0.95°C for the LSTM at thirty buildings-a 51%reduction in prediction error.Error analysis across envelope heat-loss coefficients(UAbuilding)further reveals that while the LSTM’s RMSE increases for high-𝑈𝐴structures,ScaleONet maintains uniformly low error.With millisecond-scale inference(approximately 4 ms per sample for thirty buildings),ScaleONet is well suited for large-scale reinforcement learning,receding-horizon optimization,and real-time model predictive control.
文摘Visual tracking is a classical computer vision problem with many applications.Efficient convolution operators(ECO)is one of the most outstanding visual tracking algorithms in recent years,it has shown great performance using discriminative correlation filter(DCF)together with HOG,color maps and VGGNet features.Inspired by new deep learning models,this paper propose a hybrid efficient convolution operators integrating fully convolution network(FCN)and residual network(ResNet)for visual tracking,where FCN and ResNet are introduced in our proposed method to segment the objects from backgrounds and extract hierarchical feature maps of objects,respectively.Compared with the traditional VGGNet,our approach has higher accuracy for dealing with the issues of segmentation and image size.The experiments show that our approach would obtain better performance than ECO in terms of precision plot and success rate plot on OTB-2013 and UAV123 datasets.
文摘Power flow adjustment is a sequential decision problem.The operator makes decisions to ensure that the power flow meets the system's operational constraints,thereby obtaining a typical operating mode power flow.However,this decision-making method relies heavily on human experience,which is inefficient when the system is complex.In addition,the results given by the current evaluation system are difficult to directly guide the intelligent power flow adjustment.In order to improve the efficiency and intelligence of power flow adjustment,this paper proposes a power flow adjustment method based on deep reinforcement learning.Combining deep reinforcement learning theory with traditional power system operation mode analysis,the concept of region mapping is proposed to describe the adjustment process,so as to analyze the process of power flow calculation and manual adjustment.Considering the characteristics of power flow adjustment,a Markov decision process model suitable for power flow adjustment is constructed.On this basis,a double Q network learning method suitable for power flow adjustment is proposed.This method can adjust the power flow according to the set adjustment route,thus improving the intelligent level of power flow adjustment.The method in this paper is tested on China Electric Power Research Institute(CEPRI)test system.
基金supported by the US National Science Foundation grant DMS-2207449supported by OSD/AFOSR MURI grant FA9550-20-1-0358。
文摘In this paper,we propose a DeepONet structure with causality to represent causal linear operators between Banach spaces of time-dependent signals.The theorem of universal approximations to nonlinear operators proposed in[5]is extended to operators with causalities,and the proposed Causality-DeepONet implements the physical causality in its framework.The proposed Causality-DeepONet considers causality(the state of the system at the current time is not affected by that of the future,but only by its current state and past history)and uses a convolution-type weight in its design.To demonstrate its effectiveness in handling the causal response of a physical system,the Causality-DeepONet is applied to learn the operator representing the response of a building due to earthquake ground accelerations.Extensive numerical tests and comparisons with some existing variants of DeepONet are carried out,and the Causality-DeepONet clearly shows its unique capability to learn the retarded dynamic responses of the seismic response operator with good accuracy.
