Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

Fast parallel algorithms for short-range molecular dynamics

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Models that depict innovation as a smooth, well-behaved linear process badly misspecify the nature and direction of the causal factors at work. Innovation is complex, uncertain, somewhat disorderly, and subject to changes of many sorts. Innovation is also difficult to measure and demands close coordination of adequate technical knowledge and excellent market judgment in order to satisfy economic, technological, and other types of constraints—all simultaneously. The process of innovation must be viewed as a series of changes in a complete system not only of hardware, but also of market environment, production facilities and knowledge, and the social contexts of the innovation organization.

An Overview of Innovation

Deep learning and its applications to machine health monitoring

Applications of machine learning to machine fault diagnosis: A review and roadmap

The tracking motion of the robot is realized based on a specific robot or relying on an expensive movement acquisition system. It has the problems of complex control procedures, lack of real-time p...

https://journals.sagepub.com/doi/pdf/10.1177/17298814211024052

Real-time motion tracking of cognitive Baxter robot based on differential inverse kinematics:

Although China has a common political system, there are distinct regional differences. Regional innovation system technology diffusion effect will promote the internal actors interaction and fasten the whole innovation system well operation and incessant develop, optimize regional industrial structure. This paper design a multi-index appraisal system, gives a method to quantitatively describe appraisal on RIS technology diffusion effect. Established an appraisal model by unascertained theory, and sets up the weight of classification index by information entropy, more valuable to application.

Analysis of technology innovation diffusion effect in regional innovation system by unascertained measurement

Current engineering design is a multi-step process proceeding from conceptual design to detailed design and to evaluation and testing. It is estimated that 60–70% of design decisions and most innovation occur in the conceptual design stage, which may include conceptual design of function, operating principles, lavout, shape, and structure. However, few computational tools are available to help designers to explore the design space and stimulate the product innovation process. As a result, product innovation is strongly constrained by the designer’s ingenuity and experience, and a systmatic approach to product innovation is strongly needed.

GPBG: A Framework for Evolutionary Design of Multi-domain Engineering Systems Using Genetic Programming and Bond Graphs

This paper investigates fast Pareto genetic algorithm based on fast fitness identification and external population updating scheme (FPGA) for searching Pareto-optimal set, which is based on a new approach of fast fitness identification algorithm for individual and a clustering on the basis of external population updating scheme to maintain population diversity and even distribution of Pareto solutions. Experiments on a set of multi-objective 0/1 knapsack optimization problems shows that FPGA can obtain high-quality, well distributed nondominated Pareto solutions with less computational efforts compared to other state-of art algorithms, and FPGA in convergence speed outperforms the representative SPEA.

Studies on Fast Pareto Genetic Algorithm Based on Fast Fitness Identification and External Population Updating Scheme

Hybrid Electric Vehicles (HEVs), surrounded by high complexity, nonlinear constraint and large amount of coupling design parameters, provides fairly higher fuel economy with lower emissions than conventional vehicles. It is significant to optimize HEV's parameters to enhance its performance. Considering the disadvantage of the methods transforming multi-objective functions into a single objective evaluation function, this paper reports a methodological approach for multi-objective optimization of parallel hybrid vehicle. Firstly, a model of parallel hybrid electric vehicle for optimal simulation is established. Secondly, based on the non-dominated sorting genetic algorithms II, a methodological approach for the simultaneous optimization of HEV parameters to minimize the fuel consumption and emissions was proposed, which adopts ADVISOR to simulate. Taking Insight as a case, the simulation results show that this approach can obtain a set of Pareto-optimal solutions with better performance.

Shaobo Li

Papers

Real-time motion tracking of cognitive Baxter robot based on differential inverse kinematics:

Analysis of technology innovation diffusion effect in regional innovation system by unascertained measurement

GPBG: A Framework for Evolutionary Design of Multi-domain Engineering Systems Using Genetic Programming and Bond Graphs

Studies on Fast Pareto Genetic Algorithm Based on Fast Fitness Identification and External Population Updating Scheme

Multi-objective optimization of constrained parallel Hybrid Electric Vehicles