Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density a...Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density and charging rates.However,traditional theoretical computational methods face the dilemma of long research cycles.Machine learning methods have in recent years shown considerable potential for accelerating research efforts.However,most approaches are limited to specific properties of particular devices.In this paper,we propose a forward prediction and screening framework for functional materials,which includes database selection,attributes,descriptors,machine learning models,and prediction and screening.Based on the Materials Project database,auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks,which finally screen 12 lithium-ion,6 zinc-ion,and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials.The results show that the proposed framework can predict material performance well toward rapid initial screening.The proposed framework can provide a specific and complete working process reference for energy materials design work,contributing to the theoretical foundation for the design of core industrial software for materials engineering.展开更多
An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise.In this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is...An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise.In this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation least squares （BCLS） methods are proposed.By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively.By exploiting the statistical properties of the cross-correlation function between the least squares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed.Simulation results are presented to illustrate the good performances of the proposed algorithms.展开更多
A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates f...A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.展开更多
Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional p...Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.展开更多
In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs,this study developed a method of designing origami-inspired braces based ...In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs,this study developed a method of designing origami-inspired braces based on machine learning models.Four geometric parameters,i.e.,cross-sectional side length,plate thickness,crease weakening coefficient,and plane angles,were used to establish a mapping relationship with five mechanical parameters,including elastic stiffness,yield load,yield displacement,ultimate load,and ultimate displacement,all of which were calculated from load-displacement curves.Firstly,forward prediction models were trained and compared for single and multiple mechanical outputs.The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships.Secondly,certain reverse prediction models were established to obtain the optimized design parameters.Finally,the design method of this study was verified in finite element methods.The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.展开更多
Classical indifference valuation,a widely studied approach in incomplete markets,uses critically the a priori knowledge of the characteristics(arrival,maturity,payoff structure)of the projects in consideration.This as...Classical indifference valuation,a widely studied approach in incomplete markets,uses critically the a priori knowledge of the characteristics(arrival,maturity,payoff structure)of the projects in consideration.This assumption,however,may not accommodate realistic scenarios in which projects,not initially anticipated,arrive at later times.To accommodate this,we employ forward indifference valuation criteria,which by construction are flexible enough to adapt to such"non-anticipated"cases while yielding time-consistent indifference prices.We consider and analyze in detail two representative cases:valuation adjustments due to incoming non-anticipated project and the relative forward indifference valuation of new projects in relation to existing ones.展开更多
基金financially supported by the Defense Industrial Technology Development Program(JCKY2021-601B019).
文摘Energy materials play an important role in renewable and green energy technologies.The exploration of new materials,including nanomaterials,is important for breaking through the current bottlenecks of energy density and charging rates.However,traditional theoretical computational methods face the dilemma of long research cycles.Machine learning methods have in recent years shown considerable potential for accelerating research efforts.However,most approaches are limited to specific properties of particular devices.In this paper,we propose a forward prediction and screening framework for functional materials,which includes database selection,attributes,descriptors,machine learning models,and prediction and screening.Based on the Materials Project database,auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks,which finally screen 12 lithium-ion,6 zinc-ion,and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials.The results show that the proposed framework can predict material performance well toward rapid initial screening.The proposed framework can provide a specific and complete working process reference for energy materials design work,contributing to the theoretical foundation for the design of core industrial software for materials engineering.
基金Supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant No 60625104)the Ministerial Foundation of China (Grant No A2220060039)the Fundamental Research Foundation of BIT (Grant No 1010050320810)
文摘An important and hard problem in signal processing is the estimation of parameters in the presence of observation noise.In this paper, adaptive finite impulse response （FIR） filtering with noisy input-output data is considered and two developed bias compensation least squares （BCLS） methods are proposed.By introducing two auxiliary estimators, the forward output predictor and the backward output predictor are constructed respectively.By exploiting the statistical properties of the cross-correlation function between the least squares （LS） error and the forward/backward prediction error, the estimate of the input noise variance is obtained; the effect of the bias can thereafter be removed.Simulation results are presented to illustrate the good performances of the proposed algorithms.
文摘A new dynamic model identification method is developed for continuous-time series analysis and forward prediction applications. The quantum of data is defined over moving time intervals in sliding window coordinates for compressing the size of stored data while retaining the resolution of information. Quantum vectors are introduced as the basis of a linear space for defining a Dynamic Quantum Operator (DQO) model of the system defined by its data stream. The transport of the quantum of compressed data is modeled between the time interval bins during the movement of the sliding time window. The DQO model is identified from the samples of the real-time flow of data over the sliding time window. A least-square-fit identification method is used for evaluating the parameters of the quantum operator model, utilizing the repeated use of the sampled data through a number of time steps. The method is tested to analyze, and forward-predict air temperature variations accessed from weather data as well as methane concentration variations obtained from measurements of an operating mine. The results show efficient forward prediction capabilities, surpassing those using neural networks and other methods for the same task.
基金supported by the National Key Research and Development Program of China under Grant No.2020YFA0710100the National Natural Science Foundation of China under Grants No.92050102,No.11874311,and No.11504306the Fundamental Research Funds for the Central Universities under Grant No.20720200074。
文摘Total transmission plays an important role in efficiency improvement and wavefront control,and has made great progress in many applications,such as the optical film and signal transmission.Therefore,many traditional physical methods represented by transformation optics have been studied to achieve total transmission.However,these methods have strict limitations on the size of the photonic structure,and the calculation is complex.Here,we exploit deep learning to achieve this goal.In deep learning,the data-driven prediction and design are carried out by artificial neural networks(ANNs),which provide a convenient architecture for large dataset problems.By taking the transmission characteristic of the multi-layer stacks as an example,we demonstrate how optical materials can be designed by using ANNs.The trained network directly establishes the mapping from optical materials to transmission spectra,and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space.Our work paves the way for the optical material design with special properties based on deep learning.
基金supported by the Jiangsu Provincial Department of Science and Technology Projects(BZ2022049 and BE2023801).
文摘In order to rapidly and accurately evaluate the mechanical properties of a novel origami-inspired tube structure with multiple parameter inputs,this study developed a method of designing origami-inspired braces based on machine learning models.Four geometric parameters,i.e.,cross-sectional side length,plate thickness,crease weakening coefficient,and plane angles,were used to establish a mapping relationship with five mechanical parameters,including elastic stiffness,yield load,yield displacement,ultimate load,and ultimate displacement,all of which were calculated from load-displacement curves.Firstly,forward prediction models were trained and compared for single and multiple mechanical outputs.The parameter ranges were extended and refined to improve the predicted results by introducing the intrinsic mechanical relationships.Secondly,certain reverse prediction models were established to obtain the optimized design parameters.Finally,the design method of this study was verified in finite element methods.The design and analysis framework proposed in this study can be used to promote the application of other novel multi-parameter structures.
文摘Classical indifference valuation,a widely studied approach in incomplete markets,uses critically the a priori knowledge of the characteristics(arrival,maturity,payoff structure)of the projects in consideration.This assumption,however,may not accommodate realistic scenarios in which projects,not initially anticipated,arrive at later times.To accommodate this,we employ forward indifference valuation criteria,which by construction are flexible enough to adapt to such"non-anticipated"cases while yielding time-consistent indifference prices.We consider and analyze in detail two representative cases:valuation adjustments due to incoming non-anticipated project and the relative forward indifference valuation of new projects in relation to existing ones.
