Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all u...Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data.In the present study,we proposed a physics-constrained neural network(PhyNN)for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks(NNs).This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN.Moreover,we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process.The performance of the proposed PhyNN model is verified using data from a case study project.Results show that our PhyNN model achieves higher prediction accuracy,better generalization ability,and robustness than the purely data-driven NN model when confronted with data containing noise and outliers.Remarkably,by incorporating physical constraints,the admissible solution space of PhyNN is significantly narrowed,leading to a substantial reduction in the need for the amount of training data.The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.展开更多
In this paper,we propose a graph model embedded with compact physical equations for modeling the thermal dynamics of buildings.The principles of heat flow across various components in the building,such as walls and do...In this paper,we propose a graph model embedded with compact physical equations for modeling the thermal dynamics of buildings.The principles of heat flow across various components in the building,such as walls and doors,fit the message-passing strategy used by Graph Neural networks(GNNs).The proposed method is to represent the multi-zone building as a graph,in which only zones are considered as nodes,and any heat flow between zones is modeled as an edge based on prior knowledge of the building structure.Furthermore,the thermal dynamics of these components are described by compact models in the graph.GNNs are further employed to train model parameters from collected data.During model training,our proposed method enforces physical constraints(e.g.,zone sizes and connections)on model parameters and propagates the penalty in the loss function of GNN.Such constraints are essential to ensure model robustness and interpretability.We evaluate the effectiveness of the proposed modeling approach on a realistic dataset with multiple zones.The results demonstrate a satisfactory accuracy in the prediction of multi-zone temperature.Moreover,we illustrate that the new model can reliably learn hidden physical parameters with incomplete data.展开更多
Data assimilation of subsurface transport is important in many energy and environmental applications,but its solution is typically challenging.In this work,we build physics-constrained deep learning models to predict ...Data assimilation of subsurface transport is important in many energy and environmental applications,but its solution is typically challenging.In this work,we build physics-constrained deep learning models to predict the full-scale hydraulic conductivity,hydraulic head,and concentration fields in porous media from sparse measure-ment of these observables.The model is developed based on convolutional neural networks with the encoding-decoding process.The model is trained by minimizing a loss function that incorporates residuals of governing equations of subsurface transport instead of using labeled data.Once trained,the model predicts the unknown conductivity,hydraulic head,and concentration fields with an average relative error<10%when the data of these observables is available at 12.2%of the grid points in the porous media.The model has a robust predictive performance for porous media with different conductivities and transport under different Péclet number(0.5<Pe<500).We also quantify the predictive uncertainty of the model and evaluate the reliability of its prediction by incorporating a variational parameter into the model.展开更多
Developing a universal model for predicting condensation heat transfer coefficients remains challenging,particularly for steam–non-condensable gas mixtures,owing to the intricate nonlinear interactions between multip...Developing a universal model for predicting condensation heat transfer coefficients remains challenging,particularly for steam–non-condensable gas mixtures,owing to the intricate nonlinear interactions between multiphase flow,heat,and mass transfer phenomena.Data-driven machine learning(ML)shows promise in efficiently and accurately predicting condensation heat transfer coefficients.Research has employed various ML methods—multilayer perceptron neural networks,convolutional-neural-network–based DenseNet,backpropagation neural networks,etc.—to investigate steam condensation with non-condensable gases.However,these exhibit limited extrapolation ability and heavily rely on data quantity owing to their black-box nature.This study proposes a physics-informed ML model that combines physical constraints derived from the modified Nusselt model with conventional data-driven ML techniques.The model's predictive performance is evaluated using a comprehensive database(879 datapoints from 13 studies).A physics-constrained and eight data-driven ML methods are assessed.The results reveal that the physics-constrained approach combined with XGBoost significantly outperforms conventional ML methods on extrapolation datasets(199 datapoints from 3 studies),achieving a mean absolute percentage error of 11.22%,which is approximately half that of the best-performing fully data-driven model at 21.63%.The model demonstrates consistent and reliable performance across diverse datasets,making it an effective tool for predicting heat transfer coefficients in steam–non-condensable gas mixtures.By deepening the understanding of the underlying physical processes,the proposed model supports the development of precise and efficient engineering solutions for condensation heat transfer.展开更多
Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy fo...Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy for the discovery of a generic physics-constrained anisotropic constitutive model for the heterogeneous hyperelastic materials.Based on the sparse multiscale experimental data,the constitutive artificial neural networks for hyperelastic component phases containing composite interfaces are established by the particle swarm optimization algorithm.A microscopic finite element coupled constitutive artificial neural networks solver is introduced to obtain the homogenized stress-stretch relation of heterogeneous materials with different microstructures.And a dense stress-stretch relation dataset is generated by training a neural network through the FE results.Further,a generic invariant representation of strain energy function(SEF)is proposed with a parameter set being implicitly expressed by artificial neural networks(SANN),which describes the hyperelastic properties of heterogeneous materials with different microstructures.A convexity constraint is imposed on the SEF to ensure that the multiscale constitutive model is physically relevant,and the ℓ_(1) regularization combined with thresholding is introduced to the loss function of SANN to improve the interpretability of this model.Finally,the multiscale model is hierarchically trained,cross-validated and tested using the experimental data of cord-rubber composite materials with different microstructures.The proposed multiscale model provides a convenient and general methodology for constitutive modeling of heterogeneous hyperelastic materials.展开更多
基金funded by the National Natural Science Foundation of China(Grant No.52090082)support provided by the China Scholarship Council(Grant No.202206260206).
文摘Accurate prediction of ground surface settlement(GSS)adjacent to an excavation is important to prevent potential damage to the surrounding environment.Previous studies have extensively delved into this topic but all under the limitations of either imprecise theories or insufficient data.In the present study,we proposed a physics-constrained neural network(PhyNN)for predicting excavation-induced GSS to fully integrate the theory of elasticity with observations and make full use of the strong fitting ability of neural networks(NNs).This model incorporates an analytical solution as an additional regularization term in the loss function to guide the training of NN.Moreover,we introduced three trainable parameters into the analytical solution so that it can be adaptively modified during the training process.The performance of the proposed PhyNN model is verified using data from a case study project.Results show that our PhyNN model achieves higher prediction accuracy,better generalization ability,and robustness than the purely data-driven NN model when confronted with data containing noise and outliers.Remarkably,by incorporating physical constraints,the admissible solution space of PhyNN is significantly narrowed,leading to a substantial reduction in the need for the amount of training data.The proposed PhyNN can be utilized as a general framework for integrating physical constraints into data-driven machine-learning models.
文摘In this paper,we propose a graph model embedded with compact physical equations for modeling the thermal dynamics of buildings.The principles of heat flow across various components in the building,such as walls and doors,fit the message-passing strategy used by Graph Neural networks(GNNs).The proposed method is to represent the multi-zone building as a graph,in which only zones are considered as nodes,and any heat flow between zones is modeled as an edge based on prior knowledge of the building structure.Furthermore,the thermal dynamics of these components are described by compact models in the graph.GNNs are further employed to train model parameters from collected data.During model training,our proposed method enforces physical constraints(e.g.,zone sizes and connections)on model parameters and propagates the penalty in the loss function of GNN.Such constraints are essential to ensure model robustness and interpretability.We evaluate the effectiveness of the proposed modeling approach on a realistic dataset with multiple zones.The results demonstrate a satisfactory accuracy in the prediction of multi-zone temperature.Moreover,we illustrate that the new model can reliably learn hidden physical parameters with incomplete data.
文摘Data assimilation of subsurface transport is important in many energy and environmental applications,but its solution is typically challenging.In this work,we build physics-constrained deep learning models to predict the full-scale hydraulic conductivity,hydraulic head,and concentration fields in porous media from sparse measure-ment of these observables.The model is developed based on convolutional neural networks with the encoding-decoding process.The model is trained by minimizing a loss function that incorporates residuals of governing equations of subsurface transport instead of using labeled data.Once trained,the model predicts the unknown conductivity,hydraulic head,and concentration fields with an average relative error<10%when the data of these observables is available at 12.2%of the grid points in the porous media.The model has a robust predictive performance for porous media with different conductivities and transport under different Péclet number(0.5<Pe<500).We also quantify the predictive uncertainty of the model and evaluate the reliability of its prediction by incorporating a variational parameter into the model.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.RS-2024–00353227 and RS-2024–00411577).
文摘Developing a universal model for predicting condensation heat transfer coefficients remains challenging,particularly for steam–non-condensable gas mixtures,owing to the intricate nonlinear interactions between multiphase flow,heat,and mass transfer phenomena.Data-driven machine learning(ML)shows promise in efficiently and accurately predicting condensation heat transfer coefficients.Research has employed various ML methods—multilayer perceptron neural networks,convolutional-neural-network–based DenseNet,backpropagation neural networks,etc.—to investigate steam condensation with non-condensable gases.However,these exhibit limited extrapolation ability and heavily rely on data quantity owing to their black-box nature.This study proposes a physics-informed ML model that combines physical constraints derived from the modified Nusselt model with conventional data-driven ML techniques.The model's predictive performance is evaluated using a comprehensive database(879 datapoints from 13 studies).A physics-constrained and eight data-driven ML methods are assessed.The results reveal that the physics-constrained approach combined with XGBoost significantly outperforms conventional ML methods on extrapolation datasets(199 datapoints from 3 studies),achieving a mean absolute percentage error of 11.22%,which is approximately half that of the best-performing fully data-driven model at 21.63%.The model demonstrates consistent and reliable performance across diverse datasets,making it an effective tool for predicting heat transfer coefficients in steam–non-condensable gas mixtures.By deepening the understanding of the underlying physical processes,the proposed model supports the development of precise and efficient engineering solutions for condensation heat transfer.
基金supported by the Natural Science Foundation of Chongqing(CSTB2022NSCQ-MSX0296)Strategic Priority Research Program of the Chinese Academy of Sciences(XDC06030102)+1 种基金National Key R&D Program of China(2020YFA0713603)National Natural Science Foundation of China(12271409).
文摘Constitutive modeling of heterogeneous hyperelastic materials is still a challenge due to their complex and variable microstructures.We propose a multiscale datadriven approach with a hierarchical learning strategy for the discovery of a generic physics-constrained anisotropic constitutive model for the heterogeneous hyperelastic materials.Based on the sparse multiscale experimental data,the constitutive artificial neural networks for hyperelastic component phases containing composite interfaces are established by the particle swarm optimization algorithm.A microscopic finite element coupled constitutive artificial neural networks solver is introduced to obtain the homogenized stress-stretch relation of heterogeneous materials with different microstructures.And a dense stress-stretch relation dataset is generated by training a neural network through the FE results.Further,a generic invariant representation of strain energy function(SEF)is proposed with a parameter set being implicitly expressed by artificial neural networks(SANN),which describes the hyperelastic properties of heterogeneous materials with different microstructures.A convexity constraint is imposed on the SEF to ensure that the multiscale constitutive model is physically relevant,and the ℓ_(1) regularization combined with thresholding is introduced to the loss function of SANN to improve the interpretability of this model.Finally,the multiscale model is hierarchically trained,cross-validated and tested using the experimental data of cord-rubber composite materials with different microstructures.The proposed multiscale model provides a convenient and general methodology for constitutive modeling of heterogeneous hyperelastic materials.
