Some perspective on the Large Scale Scientific Computation Research

The "Large Scale Scientific Computation (LSSC) Research"project is one of the State Major Basic Research projects funded by the Chinese Ministry of Science and Technology in the field ofinformation science and technology.……

A Review and Prospect for Scientific and Engineering Computing in China

The rise of scientific computing was one of the most important advances in the S&T progress during the second half of the 20th century. Parallel with theoretical exploration and scientific experiments,scientific computing has become the 'third means' for scientific activities in the world today. The article gives a panoramic review of the subject during the past 50 years in China and lists the contributions made by Chinese scientists in this field. In addition, it reveals some key contents of related projects in the national research plan and looks into the development vista for the subject in China at the dawning years of the new century.

The coupled deep neural networks for coupling of the Stokes and Darcy–Forchheimer problems

We present an efficient deep learning method called coupled deep neural networks(CDNNs) for coupling of the Stokes and Darcy–Forchheimer problems. Our method compiles the interface conditions of the coupled problems into the networks properly and can be served as an efficient alternative to the complex coupled problems. To impose energy conservation constraints, the CDNNs utilize simple fully connected layers and a custom loss function to perform the model training process as well as the physical property of the exact solution. The approach can be beneficial for the following reasons: Firstly, we sample randomly and only input spatial coordinates without being restricted by the nature of samples.Secondly, our method is meshfree, which makes it more efficient than the traditional methods. Finally, the method is parallel and can solve multiple variables independently at the same time. We present the theoretical results to guarantee the convergence of the loss function and the convergence of the neural networks to the exact solution. Some numerical experiments are performed and discussed to demonstrate performance of the proposed method.

Local Radial Basis Function Methods: Comparison, Improvements, and Implementation

Radial Basis Function methods for scattered data interpolation and for the numerical solution of PDEs were originally implemented in a global manner. Subsequently, it was realized that the methods could be implemented more efficiently in a local manner and that the local approaches could match or even surpass the accuracy of the global implementations. In this work, three localization approaches are compared: a local RBF method, a partition of unity method, and a recently introduced modified partition of unity method. A simple shape parameter selection method is introduced and the application of artificial viscosity to stabilize each of the local methods when approximating time-dependent PDEs is reviewed. Additionally, a new type of quasi-random center is introduced which may be better choices than other quasi-random points that are commonly used with RBF methods. All the results within the manuscript are reproducible as they are included as examples in the freely available Python Radial Basis Function Toolbox.

Visualization Techniques in Electromagnetic Engineering

It briefly describes the techniques of Visualization in Scientific Computation (ViSC). Combining Open GL, a 3D graphic library, we discuss and analyze some visualization techniques in electromagnetic engineering.

被动锁模掺铒光纤激光器自相似脉冲的啁啾提取(英文)

In this paper, we adopt cloud computing in a specific scientific computing field for its virtualization, distribution and dynamic extendibility as follows: We obtain high-energy parabolic self-similar pulses by numerical simulation using our non-distributed passively mode-locked Er-doped fiber laser model. For researching characteristics of these wave-breaking-free self-similar pulses, chirp of them must be extracted. We propose several time-frequency analysis methods adopted in chirp extraction of ultra-short optical pulses for the first time and discuss the advantages and disadvantages of them in this particular application.

An Advanced Analysis of Cloud Computing Concepts Based on the Computer Science Ontology

Our primary research hypothesis stands on a simple idea:The evolution of top-rated publications on a particular theme depends heavily on the progress and maturity of related topics.And this even when there are no clear relations or some concepts appear to cease to exist and leave place for newer ones starting many years ago.We implemented our model based on Computer Science Ontology(CSO)and analyzed 44 years of publications.Then we derived the most important concepts related to Cloud Computing(CC)from the scientific collection offered by Clarivate Analytics.Our methodology includes data extraction using advanced web crawling techniques,data preparation,statistical data analysis,and graphical representations.We obtained related concepts after aggregating the scores using the Jaccard coefficient and CSO Ontology.Our article reveals the contribution of Cloud Computing topics in research papers in leading scientific journals and the relationships between the field of Cloud Computing and the interdependent subdivisions identified in the broader framework of Computer Science.

VISUALIZATION OF THREE-DIMENSIONAL DATA FIELD AND ITS APPLICATION IN MACHINE TESTING

In order to realize visualization of three-dimensional data field （TDDF） in instrument, two methods of visualization of TDDF and the usual manner of quick graphic and image processing are analyzed. And how to use OpenGL technique and the characteristic of analyzed data to construct a TDDF, the ways of reality processing and interactive processing are described. Then the medium geometric element and a related realistic model are constructed by means of the first algorithm. Models obtained for attaching the third dimension in three-dimensional data field are presented. An example for TDDF realization of machine measuring is provided. The analysis of resultant graphic indicates that the three-dimensional graphics built by the method developed is featured by good reality, fast processing and strong interaction

Modeling and scientific computing for the transmission dynamics of Avian influenza with half-saturated incidence

This paper presents the mathematical analysis of the dynamical system for avian influenza.The proposed model considers a nonlinear dynamical model of birds and human.The half-saturated incidence rate is used for the transmission of avian influenza infection.Rigorous mathematical results are presented for the proposed models.The local and global dynamics of each model are presented and proven that when R0<1,then the disease-free equilibrium of each model is stable both locally and globally,and when R0>1,then the endemic equilibrium is stable both locally and globally.The numerical results obtained for the proposed model shows that influenza could be eliminated from the community if the threshold is not greater than unity.

An Adaptive Physics-Informed Neural Network with Two-Stage Learning Strategy to Solve Partial Differential Equations

Physics-Informed Neural Network(PINN)represents a new approach to solve Partial Differential Equations(PDEs).PINNs aim to solve PDEs by integrating governing equations and the initial/boundary conditions(I/BCs)into a loss function.However,the imbalance of the loss function caused by parameter settings usually makes it difficult for PINNs to converge,e.g.because they fall into local optima.In other words,the presence of balanced PDE loss,initial loss and boundary loss may be critical for the convergence.In addition,existing PINNs are not able to reveal the hidden errors caused by non-convergent boundaries and conduction errors caused by the PDE near the boundaries.Overall,these problems have made PINN-based methods of limited use on practical situations.In this paper,we propose a novel physics-informed neural network,i.e.an adaptive physics-informed neural network with a two-stage training process.Our algorithm adds spatio-temporal coefficient and PDE balance parameter to the loss function,and solve PDEs using a two-stage training process:pre-training and formal training.The pre-training step ensures the convergence of boundary loss,whereas the formal training process completes the solution of PDE by balancing various loss functions.In order to verify the performance of our method,we consider the imbalanced heat conduction and Helmholtz equations often appearing in practical situations.The Klein-Gordon equation,which is widely used to compare performance,reveals that our method is able to reduce the hidden errors.Experimental results confirm that our algorithm can effectively and accurately solve models with unbalanced loss function,hidden errors and conduction errors.The codes developed in this manuscript are publicy available at https://github.com/callmedrcom/ATPINN.

Toward memristive in-memory computing:principles and applications

With the rapid growth of computer science and big data,the traditional von Neumann architecture suffers the aggravating data communication costs due to the separated structure of the processing units and memories.Memristive in-memory computing paradigm is considered as a prominent candidate to address these issues,and plentiful applications have been demonstrated and verified.These applications can be broadly categorized into two major types:soft computing that can tolerant uncertain and imprecise results,and hard computing that emphasizes explicit and precise numerical results for each task,leading to different requirements on the computational accuracies and the corresponding hardware solutions.In this review,we conduct a thorough survey of the recent advances of memristive in-memory computing applications,both on the soft computing type that focuses on artificial neural networks and other machine learning algorithms,and the hard computing type that includes scientific computing and digital image processing.At the end of the review,we discuss the remaining challenges and future opportunities of memristive in-memory computing in the incoming Artificial Intelligence of Things era.

Discrete biological modeling for the immune response to dengue virus

Dengue infection affects more than half of the world’s population,with 1 billion symp-tomatic cases identified per year and several distinct genetic serotypes:DENV 1–4.Transmitted via the mosquito bite,the dengue virus infects Langerhans cells.Monocytes,B lymphocytes,and mast cells infected with dengue virus produce various cytokines although it is not clear which ones are predominant during DHF disease.A mathemat-ical model of the Dengue virus infection is developed according to complex dynamics determined by many factors.Starting from a state of equilibrium that we could define as“virus-free”asymptotically stable with a viral reproduction number lower than one which means a very effective action of the innate immune system:it stops the infectious process,the mathematical analysis of stability in the presence of the virus demonstrates that the proposed model is dynamically influenced.Dengue fever affects more than half of the world’s population,with 1 billion symptomatic cases and multiple genetic serotypes confirmed each year,which simulates a network of interactions between the various populations involved without considering the speeds of the processes in question which are indicated in a separate computation.In this research,a hybrid approach of petri nets is utilized to connect the discrete models of dengue.