Predicting thermal conductance of complex networks poses a formidable challenge in the field of materials science and engineering. This challenge arises due to the intricate interplay between the parameters of network...Predicting thermal conductance of complex networks poses a formidable challenge in the field of materials science and engineering. This challenge arises due to the intricate interplay between the parameters of network structure and thermal conductance, encompassing connectivity, network topology, network geometry, node inhomogeneity, and others. Our understanding of how these parameters specifically influence heat transfer performance remains limited. Deep learning offers a promising approach for addressing such complex problems. We find that the well-established convolutional neural network models AlexNet can predict the thermal conductance of complex network efficiently. Our approach further optimizes the calculation efficiency by reducing the image recognition in consideration that the thermal transfer is inherently encoded within the Laplacian matrix.Intriguingly, our findings reveal that adopting a simpler convolutional neural network architecture can achieve a comparable prediction accuracy while requiring less computational time. This result facilitates a more efficient solution for predicting the thermal conductance of complex networks and serves as a reference for machine learning algorithm in related domains.展开更多
In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous beha...In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous behaviors. We refine the theoretical index leverage model proposed by Reigneron et al., to exactly quantify the meta-correlation under various levels of price fluctuations [Reigneron P A, Allez R and Bouchaud J P 2011 Physica A 390 3026]. The characteristics of meta-correlations in times of market losses, are found to be significantly different in Chinese and American financial markets. In addition,unlike the asymmetric results at the daily scale, the correlation behaviors are found to be symmetric at the high-frequency scale.展开更多
Due to its great potential applications in thermal management,heat control,and quantum information,phononics has gained increasing attentions since the first publication in Rev.Mod.Phys.841045(2012).Many theoretical a...Due to its great potential applications in thermal management,heat control,and quantum information,phononics has gained increasing attentions since the first publication in Rev.Mod.Phys.841045(2012).Many theoretical and experimental progresses have been achieved in the past decade.In this paper,we first give a critical review of the progress in thermal diodes and transistors,especially in classical regime.Then,we give a brief introduction to the new developing research directions such as topological phononics and quantum phononics.In the third part,we discuss the potential applications.Last but not least,we point out the outlook and challenges ahead.展开更多
For thermal conduction cases,one can detect the size of an object explicitly by measuring the temperature distribution around it.If the temperature is the only signature we can obtain,we will give an incorrect judgmen...For thermal conduction cases,one can detect the size of an object explicitly by measuring the temperature distribution around it.If the temperature is the only signature we can obtain,we will give an incorrect judgment on the shape or size of the object by disturbing the distribution of it.According to this principle,in this article,we develop a transformation method and design a dual-functional thermal device,which can create a thermal illusion thai the object inside it"seems"to appear bigger or smaller than its original size.This device can functionally switch among magnifier and minifier at will.The proposed device consists of two layers:the cloak and the complementary material.A thermal cloak can make the internal region thermally"invisible"while the complementary layer offsets this effect.The combination leads to the illusion of magnification and minification.As a result of finite element simulations,the performances of the illusions are confirmed.展开更多
基金supported by the National Natural Science Foundation of China (Grant Nos. 52250191 and 12205138)the Shenzhen Science and Technology Innovation Committee (SZSTI) (Grant/Award No. JCYJ20220530113206015)。
文摘Predicting thermal conductance of complex networks poses a formidable challenge in the field of materials science and engineering. This challenge arises due to the intricate interplay between the parameters of network structure and thermal conductance, encompassing connectivity, network topology, network geometry, node inhomogeneity, and others. Our understanding of how these parameters specifically influence heat transfer performance remains limited. Deep learning offers a promising approach for addressing such complex problems. We find that the well-established convolutional neural network models AlexNet can predict the thermal conductance of complex network efficiently. Our approach further optimizes the calculation efficiency by reducing the image recognition in consideration that the thermal transfer is inherently encoded within the Laplacian matrix.Intriguingly, our findings reveal that adopting a simpler convolutional neural network architecture can achieve a comparable prediction accuracy while requiring less computational time. This result facilitates a more efficient solution for predicting the thermal conductance of complex networks and serves as a reference for machine learning algorithm in related domains.
基金supported by the National Natural Science Foundation of China(Grant No.11222544)the Fok Ying Tung Education Foundation(Grant No.131008)the Program for New Century Excellent Talents in University,China(Grant No.NCET-12-0121)
文摘In financial markets, the relation between fluctuations of stock prices and trading behaviors is complex. It is intriguing to quantify this kind of meta-correlation between market fluctuations and the synchronous behaviors. We refine the theoretical index leverage model proposed by Reigneron et al., to exactly quantify the meta-correlation under various levels of price fluctuations [Reigneron P A, Allez R and Bouchaud J P 2011 Physica A 390 3026]. The characteristics of meta-correlations in times of market losses, are found to be significantly different in Chinese and American financial markets. In addition,unlike the asymmetric results at the daily scale, the correlation behaviors are found to be symmetric at the high-frequency scale.
基金supported by the National Natural Science Foundation of China(Grant No.62004211)Shenzhen Science and Technology Program(Grant No.RCBS20200714114858221)
文摘Due to its great potential applications in thermal management,heat control,and quantum information,phononics has gained increasing attentions since the first publication in Rev.Mod.Phys.841045(2012).Many theoretical and experimental progresses have been achieved in the past decade.In this paper,we first give a critical review of the progress in thermal diodes and transistors,especially in classical regime.Then,we give a brief introduction to the new developing research directions such as topological phononics and quantum phononics.In the third part,we discuss the potential applications.Last but not least,we point out the outlook and challenges ahead.
基金Support by the National Natural Science Foundation of China under Grant No.11222544by the Fok Ying Tung Education Foundation under Grant No.131008+1 种基金by the Program for New Century Excellent Talents in University(NCET-12-0121)by the Chinese National Key Basic Research Special Fund under Grant No.2011CB922004
文摘For thermal conduction cases,one can detect the size of an object explicitly by measuring the temperature distribution around it.If the temperature is the only signature we can obtain,we will give an incorrect judgment on the shape or size of the object by disturbing the distribution of it.According to this principle,in this article,we develop a transformation method and design a dual-functional thermal device,which can create a thermal illusion thai the object inside it"seems"to appear bigger or smaller than its original size.This device can functionally switch among magnifier and minifier at will.The proposed device consists of two layers:the cloak and the complementary material.A thermal cloak can make the internal region thermally"invisible"while the complementary layer offsets this effect.The combination leads to the illusion of magnification and minification.As a result of finite element simulations,the performances of the illusions are confirmed.
