In this study,we propose a constraint learning strategy based on interpretability analysis to improve the convergence and accuracy of the enriched physics-informed neural network(EPINN),which is applied to simulate tw...In this study,we propose a constraint learning strategy based on interpretability analysis to improve the convergence and accuracy of the enriched physics-informed neural network(EPINN),which is applied to simulate two-phase flow in heterogeneous porous media.Specifically,we first analyze the layerwise outputs of EPINN,and identify the distinct functions across layers,including dimensionality adjustment,pointwise construction of non-equilibrium potential,extraction of high-level features,and the establishment of long-range dependencies.Then,inspired by these distinct modules,we propose a novel constraint learning strategy based on regularization approaches,which improves neural network(NN)learning through layer-specific differentiated updates to enhance cross-timestep generalization.Since different neu ral network layers exhibit varying sensitivities to global generalization and local regression,we decrease the update frequency of layers more sensitive to local learning under this constraint learning strategy.In other words,the entire neural network is encouraged to extract more generalized features.The superior performance of the proposed learning strategy is validated through evaluations on numerical examples with varying computational complexities.Post hoc analysis reveals that gradie nt propagation exhibits more pronounced staged characte ristics,and the partial differential equation(PDE)residuals are more uniformly distributed under the constraint guidance.Interpretability analysis of the adaptive constraint process suggests that maintaining a stable information compression mode facilitates progressive convergence acceleration.展开更多
The authors regret that the citation of references in Table 1 contains error.Therefore,the authors would like to make the following correc-tion.The major parts of amendments are in bold style:The revised Table 1 The r...The authors regret that the citation of references in Table 1 contains error.Therefore,the authors would like to make the following correc-tion.The major parts of amendments are in bold style:The revised Table 1 The revised references[60]Zayakin OV,Renev DS.Density of chrome-nickel ferroalloys.KnE Materials Science 2019;5(1):297-303.https://doi.org/10.18502/kms.v5i1.3981.展开更多
A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by le...A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion.To further quantify the abstract features that characterize the data,a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance.Finally,the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty,and better performance is obtained.Experiments show that compared with the local similarity algorithm,the average precision is improved by 3.34%.Compared with the single-layer dictionary learning algorithm,the one-error is improved by 1.00%,and the average precision is improved by 0.54%.Experiments also show that it has better performance on general datasets.展开更多
Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential rel...Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.展开更多
Concentrating Solar Power Tower(CSPT)plants rely on heliostat fields to focus sunlight onto a central receiver.Although simple aiming strategies,such as directing all heliostats to the receiver’s equator,can maximize...Concentrating Solar Power Tower(CSPT)plants rely on heliostat fields to focus sunlight onto a central receiver.Although simple aiming strategies,such as directing all heliostats to the receiver’s equator,can maximize energy collection,they often result in uneven flux distributions that cause hotspots,thermal stresses,and reduced receiver lifetimes.This paper presents a novel,data-driven approach that combines constraint learning,neural network-based surrogates,and mathematical optimization to address these challenges.The methodology learns complex heliostat-to-receiver flux interactions from simulation data and embeds the resulting surrogate model in a tractable optimization framework.By maximizing a tailored quality score that balances energy collection with flux uniformity,the approach produces smoothly distributed flux profiles and mitigates excessive thermal peaks.An iterative refinement process,guided by a trust region strategy and progressive data sampling,ensures continual improvement of the surrogate model by exploring new solution spaces at each iteration.Results from a real CSPT case study show that the proposed approach outperforms conventional heuristic methods,delivering flatter flux distributions with nearly a 10%reduction in peak values and safer thermal conditions(reflected by up to a 50%decrease in deviations from safe concentration distributions),without significantly compromising overall energy capture.展开更多
基金supported by the National Key R&D Program of China(No.2023YFB4104200)the National Natural Science Foundation of China(Nos.52474067,52441411,52325402,52034010,and12131014)+2 种基金the Natural Science Foundation of Shandong Province,China(No.ZR2024ME005)Fundamental Research Funds for the Central Universities(Nos.25CX02025A and 21CX06031A)the Youth Innovation and Technology Support Program for Higher Education Institutions of Shandong Province,China(No.2022KJ070)。
文摘In this study,we propose a constraint learning strategy based on interpretability analysis to improve the convergence and accuracy of the enriched physics-informed neural network(EPINN),which is applied to simulate two-phase flow in heterogeneous porous media.Specifically,we first analyze the layerwise outputs of EPINN,and identify the distinct functions across layers,including dimensionality adjustment,pointwise construction of non-equilibrium potential,extraction of high-level features,and the establishment of long-range dependencies.Then,inspired by these distinct modules,we propose a novel constraint learning strategy based on regularization approaches,which improves neural network(NN)learning through layer-specific differentiated updates to enhance cross-timestep generalization.Since different neu ral network layers exhibit varying sensitivities to global generalization and local regression,we decrease the update frequency of layers more sensitive to local learning under this constraint learning strategy.In other words,the entire neural network is encouraged to extract more generalized features.The superior performance of the proposed learning strategy is validated through evaluations on numerical examples with varying computational complexities.Post hoc analysis reveals that gradie nt propagation exhibits more pronounced staged characte ristics,and the partial differential equation(PDE)residuals are more uniformly distributed under the constraint guidance.Interpretability analysis of the adaptive constraint process suggests that maintaining a stable information compression mode facilitates progressive convergence acceleration.
文摘The authors regret that the citation of references in Table 1 contains error.Therefore,the authors would like to make the following correc-tion.The major parts of amendments are in bold style:The revised Table 1 The revised references[60]Zayakin OV,Renev DS.Density of chrome-nickel ferroalloys.KnE Materials Science 2019;5(1):297-303.https://doi.org/10.18502/kms.v5i1.3981.
基金supported by the National Key Research and Development Project(2021YFF0901701)。
文摘A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination(JGCFD-MDL)for image classification tasks was proposed.The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion.To further quantify the abstract features that characterize the data,a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance.Finally,the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty,and better performance is obtained.Experiments show that compared with the local similarity algorithm,the average precision is improved by 3.34%.Compared with the single-layer dictionary learning algorithm,the one-error is improved by 1.00%,and the average precision is improved by 0.54%.Experiments also show that it has better performance on general datasets.
基金mainly supported by the National Natural Science Foundation of China(Nos.61125201,61303070,and U1435219)
文摘Instance-specific algorithm selection technologies have been successfully used in many research fields,such as constraint satisfaction and planning. Researchers have been increasingly trying to model the potential relations between different candidate algorithms for the algorithm selection. In this study, we propose an instancespecific algorithm selection method based on multi-output learning, which can manage these relations more directly.Three kinds of multi-output learning methods are used to predict the performances of the candidate algorithms：（1）multi-output regressor stacking;（2） multi-output extremely randomized trees; and（3） hybrid single-output and multioutput trees. The experimental results obtained using 11 SAT datasets and 5 Max SAT datasets indicate that our proposed methods can obtain a better performance over the state-of-the-art algorithm selection methods.
基金supported by the Madrid Government(Comu-nidad de Madrid-Spain)under the Multiannual Agreement with UC3M(SOLAROPIA-CM-UC3M)financial support from MCIN/AEI/10.13039/501100011033,project PID2023-151013NB-I00the FPU grant(FPU20/00916).
文摘Concentrating Solar Power Tower(CSPT)plants rely on heliostat fields to focus sunlight onto a central receiver.Although simple aiming strategies,such as directing all heliostats to the receiver’s equator,can maximize energy collection,they often result in uneven flux distributions that cause hotspots,thermal stresses,and reduced receiver lifetimes.This paper presents a novel,data-driven approach that combines constraint learning,neural network-based surrogates,and mathematical optimization to address these challenges.The methodology learns complex heliostat-to-receiver flux interactions from simulation data and embeds the resulting surrogate model in a tractable optimization framework.By maximizing a tailored quality score that balances energy collection with flux uniformity,the approach produces smoothly distributed flux profiles and mitigates excessive thermal peaks.An iterative refinement process,guided by a trust region strategy and progressive data sampling,ensures continual improvement of the surrogate model by exploring new solution spaces at each iteration.Results from a real CSPT case study show that the proposed approach outperforms conventional heuristic methods,delivering flatter flux distributions with nearly a 10%reduction in peak values and safer thermal conditions(reflected by up to a 50%decrease in deviations from safe concentration distributions),without significantly compromising overall energy capture.
