Ⅰ. REVIEW The Institute of Theoretical Physics (ITP) of the CAS was founded in 1978 with the personal approval of Comrade Deng Xiaoping, in the same year when the Third Plenary Session of the Central Committee of the...Ⅰ. REVIEW The Institute of Theoretical Physics (ITP) of the CAS was founded in 1978 with the personal approval of Comrade Deng Xiaoping, in the same year when the Third Plenary Session of the Central Committee of the CPC (Communist Party of China) was convened. Over the past 20 years, the Institute has been striving to explore a road for sound development in a bid to achieve the following targets: In regard to research, the institute strives展开更多
Partial discharge(PD)activity is an indicator of insulation deterioration and by extension,the reliability of power lines.Existing data-driven methods,while helpful,treat PD detection as a binary classification proble...Partial discharge(PD)activity is an indicator of insulation deterioration and by extension,the reliability of power lines.Existing data-driven methods,while helpful,treat PD detection as a binary classification problem,thereby failing to provide physical information(e.g.,filter PD pulse),and often provide results that conradict physical knowledge.To tackle this challenge,this paper develops a physics-informed temporal convolutional network(PITCN)for PD diagnosis(i.e.,PD detection and PD pulse filtering).During training,physical knowledge of the background noise and PD pulse identification is integrated into a learning model.Once the model is trained,the PITCN can automatically detect PD activity from time-series voltage signals with different background noises and filter PD pulses.Experimental results demonstrate that the developed PITCN outperforms the rest of the data-driven methods implemented,and in particular,the Matthews correlation coefficient of PITCN surpasses the conventional temporal convolutional network by 0.21.展开更多
Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have le...Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.展开更多
文摘Ⅰ. REVIEW The Institute of Theoretical Physics (ITP) of the CAS was founded in 1978 with the personal approval of Comrade Deng Xiaoping, in the same year when the Third Plenary Session of the Central Committee of the CPC (Communist Party of China) was convened. Over the past 20 years, the Institute has been striving to explore a road for sound development in a bid to achieve the following targets: In regard to research, the institute strives
基金supported by the Centre for Advances in Reliability and Safety(CAiRS)admitted under AIR@InnoHK Research Cluster。
文摘Partial discharge(PD)activity is an indicator of insulation deterioration and by extension,the reliability of power lines.Existing data-driven methods,while helpful,treat PD detection as a binary classification problem,thereby failing to provide physical information(e.g.,filter PD pulse),and often provide results that conradict physical knowledge.To tackle this challenge,this paper develops a physics-informed temporal convolutional network(PITCN)for PD diagnosis(i.e.,PD detection and PD pulse filtering).During training,physical knowledge of the background noise and PD pulse identification is integrated into a learning model.Once the model is trained,the PITCN can automatically detect PD activity from time-series voltage signals with different background noises and filter PD pulses.Experimental results demonstrate that the developed PITCN outperforms the rest of the data-driven methods implemented,and in particular,the Matthews correlation coefficient of PITCN surpasses the conventional temporal convolutional network by 0.21.
基金support of the National Natural Science Foundation of China(Grant Nos.12104356 and52250191)China Postdoctoral Science Foundation(Grant No.2022M712552)+2 种基金the Opening Project of Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology(Grant No.Ammt2022B-1)the Fundamental Research Funds for the Central Universitiessupport by HPC Platform,Xi’an Jiaotong University。
文摘Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.
