Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different ...Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different models exhibit distinct strengths and preferences,resulting in varying levels of performance.In this paper,we compare the capabilities of the most advanced LLMs—DeepSeek,ChatGPT,and Claude—along with their reasoning-optimized versions in addressing computational challenges.Specifically,we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems.We designed all our experiments so that a nontrivial decision is required,e.g,defining the proper space of input functions for neural operator learning.Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems,with ChatGPT o3-mini-high generally offering the fastest reasoning speed.展开更多
1.Introduction Since the publication of our original study comparing large language models(LLMs)in scientific computing and scientific machine learning tasks,Anthropic has released Claude 4.0[1],a major upgrade in its...1.Introduction Since the publication of our original study comparing large language models(LLMs)in scientific computing and scientific machine learning tasks,Anthropic has released Claude 4.0[1],a major upgrade in its Claude family of LLMs.Claude 4.0 is designed to introduce substantial improvements in reasoning,coding,and mathematical capabilities.展开更多
Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple Q...Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.展开更多
Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic i...Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic idea is to find out, from vast historical system input-output data sets, some data sets matching with the current working point, then to develop a local model using Local Polynomial Fitting (LPF) algorithm. With the change of working points, multiple local models are built, which realize the exact modeling for the global system. By comparing to other methods, the simulation results show good performance for its simple, effective and reliable estimation.展开更多
The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is...The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.展开更多
Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational schedu...Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved effectively.展开更多
Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficie...Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.展开更多
A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint o...A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine.展开更多
This article seeks to outline an integrated and practical geometric optimization design system （GODS） incorporating hybrid graphical electromagnetic computing-wedge modeling （GRECO-WM） scheme and the genetic algor...This article seeks to outline an integrated and practical geometric optimization design system （GODS） incorporating hybrid graphical electromagnetic computing-wedge modeling （GRECO-WM） scheme and the genetic algorithm （GA） for calculating the radar cross section （RCS） and optimizing the geometric parameters of a large and complex target respectively. A new wedge modeling （WM） scheme is presented for calculating the high-frequency RCS of wedge with only one visible facet based on the method of equivalent currents （MEC）. The applications of GODS to 2D cross-section and 3D surface are respectively implemented by choosing an average of monostatic RCS values corresponding to a series of incident angles over a frequency band as the optimum objective function. And the results demonstrate that the RCS can be effectively and conveniently reduced by the GODS presented in this article.展开更多
In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of faul...In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.展开更多
Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges,...Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.展开更多
Unravelling the source of quantum computing power has been a major goal in the field of quantum information science.In recent years,the quantum resource theory(QRT)has been established to characterize various quantum ...Unravelling the source of quantum computing power has been a major goal in the field of quantum information science.In recent years,the quantum resource theory(QRT)has been established to characterize various quantum resources,yet their roles in quantum computing tasks still require investigation.The so-called universal quantum computing model(UQCM),e.g.the circuit model,has been the main framework to guide the design of quantum algorithms,creation of real quantum computers etc.In this work,we combine the study of UQCM together with QRT.We find,on one hand,using QRT can provide a resource-theoretic characterization of a UQCM,the relation among models and inspire new ones,and on the other hand,using UQCM offers a framework to apply resources,study relation among these resources and classify them.We develop the theory of universal resources in the setting of UQCM,and find a rich spectrum of UQCMs and the corresponding universal resources.Depending on a hierarchical structure of resource theories,we find models can be classified into families.In this work,we study three natural families of UQCMs in detail:the amplitude family,the quasi-probability family,and the Hamiltonian family.They include some well known models,like the measurement-based model and adiabatic model,and also inspire new models such as the contextual model that we introduce.Each family contains at least a triplet of models,and such a succinct structure of families of UQCMs offers a unifying picture to investigate resources and design models.It also provides a rigorous framework to resolve puzzles,such as the role of entanglement versus interference,and unravel resource-theoretic features of quantum algorithms.展开更多
Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-ve...Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-vector product,matrix-matrix product,the sum of two matrices,and the calculation of determinant and inverse matrix.We encode the matrix entries into the probability amplitudes of the pure initial states of senders.After applying proper unitary transformation to the complete quantum system,the desired result can be found in certain blocks of the receiver’s density matrix.These quantum protocols can be used as subroutines in other quantum schemes.Furthermore,we present an alternative quantum algorithm for solving linear systems of equations.展开更多
This article describes the development of an application for generating tonal melodies. The goal of the project is to ascertain our current understanding of tonal music by means of algorithmic music generation. The me...This article describes the development of an application for generating tonal melodies. The goal of the project is to ascertain our current understanding of tonal music by means of algorithmic music generation. The method followed consists of four stages: 1) selection of music-theoretical insights, 2) translation of these insights into a set of principles, 3) conversion of the principles into a computational model having the form of an algorithm for music generation, 4) testing the “music” generated by the algorithm to evaluate the adequacy of the model. As an example, the method is implemented in Melody Generator, an algorithm for generating tonal melodies. The program has a structure suited for generating, displaying, playing and storing melodies, functions which are all accessible via a dedicated interface. The actual generation of melodies, is based in part on constraints imposed by the tonal context, i.e. by meter and key, the settings of which are controlled by means of parameters on the interface. For another part, it is based upon a set of construction principles including the notion of a hierarchical organization, and the idea that melodies consist of a skeleton that may be elaborated in various ways. After these aspects were implemented as specific sub-algorithms, the device produces simple but well-structured tonal melodies.展开更多
First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorit...First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.展开更多
基金supported by the ONR Vannevar Bush Faculty Fellowship(Grant No.N00014-22-1-2795).
文摘Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different models exhibit distinct strengths and preferences,resulting in varying levels of performance.In this paper,we compare the capabilities of the most advanced LLMs—DeepSeek,ChatGPT,and Claude—along with their reasoning-optimized versions in addressing computational challenges.Specifically,we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems.We designed all our experiments so that a nontrivial decision is required,e.g,defining the proper space of input functions for neural operator learning.Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems,with ChatGPT o3-mini-high generally offering the fastest reasoning speed.
文摘1.Introduction Since the publication of our original study comparing large language models(LLMs)in scientific computing and scientific machine learning tasks,Anthropic has released Claude 4.0[1],a major upgrade in its Claude family of LLMs.Claude 4.0 is designed to introduce substantial improvements in reasoning,coding,and mathematical capabilities.
基金the National Natural Science Foundation of China (60402028, 60672137) Wuhan Yonger Dawning Foundation (20045006071-15)China Specialized Research Fund for the Doctoral Program of Higher Eduction (20060497015).
文摘Multiple QoS modeling and algorithm in grid system is considered. Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions. Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems： optimization of grid user and grid resource provider. Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.
基金This project was supported by National Natural Science Foundation (No. 69934020).
文摘Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic idea is to find out, from vast historical system input-output data sets, some data sets matching with the current working point, then to develop a local model using Local Polynomial Fitting (LPF) algorithm. With the change of working points, multiple local models are built, which realize the exact modeling for the global system. By comparing to other methods, the simulation results show good performance for its simple, effective and reliable estimation.
文摘The traditional collaborative filtering recommendation technology has some shortcomings in the large data environment. To solve this problem, a personalized recommendation method based on cloud computing technology is proposed. The large data set and recommendation computation are decomposed into parallel processing on multiple computers. A parallel recommendation engine based on Hadoop open source framework is established, and the effectiveness of the system is validated by learning recommendation on an English training platform. The experimental results show that the scalability of the recommender system can be greatly improved by using cloud computing technology to handle massive data in the cluster. On the basis of the comparison of traditional recommendation algorithms, combined with the advantages of cloud computing, a personalized recommendation system based on cloud computing is proposed.
文摘Algorithm research of task scheduling is one of the key techniques in grid computing. This paper firstly describes a DAG task scheduling model used in grid computing environment, secondly discusses generational scheduling (GS) and communication inclusion generational scheduling (CIGS) algorithms. Finally, an improved CIGS algorithm is proposed to use in grid computing environment, and it has been proved effectively.
基金supported by 2020 Foshan Science and Technology Project(Numbering:2020001005356),Baoling Qin received the grant.
文摘Although the Internet of Things has been widely applied,the problems of cloud computing in the application of digital smart medical Big Data collection,processing,analysis,and storage remain,especially the low efficiency of medical diagnosis.And with the wide application of the Internet of Things and Big Data in the medical field,medical Big Data is increasing in geometric magnitude resulting in cloud service overload,insufficient storage,communication delay,and network congestion.In order to solve these medical and network problems,a medical big-data-oriented fog computing architec-ture and BP algorithm application are proposed,and its structural advantages and characteristics are studied.This architecture enables the medical Big Data generated by medical edge devices and the existing data in the cloud service center to calculate,compare and analyze the fog node through the Internet of Things.The diagnosis results are designed to reduce the business processing delay and improve the diagnosis effect.Considering the weak computing of each edge device,the artificial intelligence BP neural network algorithm is used in the core computing model of the medical diagnosis system to improve the system computing power,enhance the medical intelligence-aided decision-making,and improve the clinical diagnosis and treatment efficiency.In the application process,combined with the characteristics of medical Big Data technology,through fog architecture design and Big Data technology integration,we could research the processing and analysis of heterogeneous data of the medical diagnosis system in the context of the Internet of Things.The results are promising:The medical platform network is smooth,the data storage space is sufficient,the data processing and analysis speed is fast,the diagnosis effect is remarkable,and it is a good assistant to doctors’treatment effect.It not only effectively solves the problem of low clinical diagnosis,treatment efficiency and quality,but also reduces the waiting time of patients,effectively solves the contradiction between doctors and patients,and improves the medical service quality and management level.
基金NationalNaturalScienceFoundationofChina (No .60 2 3 40 2 0 )
文摘A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine.
基金National Natural Science Foundation of China （20095251024）
文摘This article seeks to outline an integrated and practical geometric optimization design system （GODS） incorporating hybrid graphical electromagnetic computing-wedge modeling （GRECO-WM） scheme and the genetic algorithm （GA） for calculating the radar cross section （RCS） and optimizing the geometric parameters of a large and complex target respectively. A new wedge modeling （WM） scheme is presented for calculating the high-frequency RCS of wedge with only one visible facet based on the method of equivalent currents （MEC）. The applications of GODS to 2D cross-section and 3D surface are respectively implemented by choosing an average of monostatic RCS values corresponding to a series of incident angles over a frequency band as the optimum objective function. And the results demonstrate that the RCS can be effectively and conveniently reduced by the GODS presented in this article.
基金the National Natural Science Foundation of China (No. 50677062)the New Century Excellent Talents in Uni-versity of China (No. NCET-07-0745)the Natural Science Foundation of Zhejiang Province, China (No. R107062)
文摘In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.
文摘Modern engineering design optimization often relies on computer simulations to evaluate candidate designs, a setup which results in expensive black-box optimization problems. Such problems introduce unique challenges, which has motivated the application of metamodel-assisted computational intelligence algorithms to solve them. Such algorithms combine a computational intelligence optimizer which employs a population of candidate solutions, with a metamodel which is a computationally cheaper approximation of the expensive computer simulation. However, although a variety of metamodels and optimizers have been proposed, the optimal types to employ are problem dependant. Therefore, a priori prescribing the type of metamodel and optimizer to be used may degrade its effectiveness. Leveraging on this issue, this study proposes a new computational intelligence algorithm which autonomously adapts the type of the metamodel and optimizer during the search by selecting the most suitable types out of a family of candidates at each stage. Performance analysis using a set of test functions demonstrates the effectiveness of the proposed algorithm, and highlights the merit of the proposed adaptation approach.
基金funded by the National Natural Science Foundation of China under Grants Nos.12047503 and 12105343.
文摘Unravelling the source of quantum computing power has been a major goal in the field of quantum information science.In recent years,the quantum resource theory(QRT)has been established to characterize various quantum resources,yet their roles in quantum computing tasks still require investigation.The so-called universal quantum computing model(UQCM),e.g.the circuit model,has been the main framework to guide the design of quantum algorithms,creation of real quantum computers etc.In this work,we combine the study of UQCM together with QRT.We find,on one hand,using QRT can provide a resource-theoretic characterization of a UQCM,the relation among models and inspire new ones,and on the other hand,using UQCM offers a framework to apply resources,study relation among these resources and classify them.We develop the theory of universal resources in the setting of UQCM,and find a rich spectrum of UQCMs and the corresponding universal resources.Depending on a hierarchical structure of resource theories,we find models can be classified into families.In this work,we study three natural families of UQCMs in detail:the amplitude family,the quasi-probability family,and the Hamiltonian family.They include some well known models,like the measurement-based model and adiabatic model,and also inspire new models such as the contextual model that we introduce.Each family contains at least a triplet of models,and such a succinct structure of families of UQCMs offers a unifying picture to investigate resources and design models.It also provides a rigorous framework to resolve puzzles,such as the role of entanglement versus interference,and unravel resource-theoretic features of quantum algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.12031004 and Grant No.12271474,61877054)the Fundamental Research Foundation for the Central Universities(Project No.K20210337)+1 种基金the Zhejiang University Global Partnership Fund,188170+194452119/003partially funded by a state task of Russian Fundamental Investigations(State Registration No.FFSG-2024-0002)。
文摘Fundamental matrix operations and solving linear systems of equations are ubiquitous in scientific investigations.Using the‘sender-receiver’model,we propose quantum algorithms for matrix operations such as matrix-vector product,matrix-matrix product,the sum of two matrices,and the calculation of determinant and inverse matrix.We encode the matrix entries into the probability amplitudes of the pure initial states of senders.After applying proper unitary transformation to the complete quantum system,the desired result can be found in certain blocks of the receiver’s density matrix.These quantum protocols can be used as subroutines in other quantum schemes.Furthermore,we present an alternative quantum algorithm for solving linear systems of equations.
文摘This article describes the development of an application for generating tonal melodies. The goal of the project is to ascertain our current understanding of tonal music by means of algorithmic music generation. The method followed consists of four stages: 1) selection of music-theoretical insights, 2) translation of these insights into a set of principles, 3) conversion of the principles into a computational model having the form of an algorithm for music generation, 4) testing the “music” generated by the algorithm to evaluate the adequacy of the model. As an example, the method is implemented in Melody Generator, an algorithm for generating tonal melodies. The program has a structure suited for generating, displaying, playing and storing melodies, functions which are all accessible via a dedicated interface. The actual generation of melodies, is based in part on constraints imposed by the tonal context, i.e. by meter and key, the settings of which are controlled by means of parameters on the interface. For another part, it is based upon a set of construction principles including the notion of a hierarchical organization, and the idea that melodies consist of a skeleton that may be elaborated in various ways. After these aspects were implemented as specific sub-algorithms, the device produces simple but well-structured tonal melodies.
基金This project was supported by Guangdong Natural Science Foundation.
文摘First of all, this paper discusses the drawbacks of multilayer perceptron (MLP), which is trained by the traditional back propagation (BP) algorithm and used in a special classification problem. A new training algorithm for neural networks based on genetic algorithm and BP algorithm is developed. The difference between the new training algorithm and BP algorithm in the ability of nonlinear approaching is expressed through an example, and the application foreground is illustrated by an example.
