Comparison of Gaussian process modeling software

Gaussian process fitting, or kriging, is often used to create a model from a set of data. Many available software packages do this, but we show that very different results can be obtained from different packages even when using the same data and model. We describe the parameterization, features, and optimization used by eight different fitting packages that run on four different platforms. We then compare these eight packages using various data functions and data sets, revealing that there are stark differences between the packages. In addition to comparing the prediction accuracy, the predictive variance--which is important for evaluating precision of predictions and is often used in stopping criteria--is also evaluated

Simulation of Water Distribution Systems

In this paper a software package offering a means of simulating complex water distribution systems is described. It has been developed in the course of our investigations into the applicability of neural networks and fuzzy systems for the implementation of decision support systems in operational control of industrial processes with case-studies taken from the water industry. Examples of how the simulation package have been used in a design and testing of the algorithms for state estimation, confidence limit analysis and fault detection are presented. Arguments for using a suitable graphical visualization techniques in solving problems like meter placement or leakage diagnosis are also given and supported by a set of examples

Data analysis with R in an experimental physics environment

A software package has been developed to bridge the R analysis model with the conceptual analysis environment typical of radiation physics experiments. The new package has been used in the context of a project for the validation of simulation models, where it has demonstrated its capability to satisfy typical requirements pertinent to the problem domain.Comment: IEEE Nuclear Science Symposium 201

ROOT - A C++ Framework for Petabyte Data Storage, Statistical Analysis and Visualization

ROOT is an object-oriented C++ framework conceived in the high-energy physics (HEP) community, designed for storing and analyzing petabytes of data in an efficient way. Any instance of a C++ class can be stored into a ROOT file in a machine-independent compressed binary format. In ROOT the TTree object container is optimized for statistical data analysis over very large data sets by using vertical data storage techniques. These containers can span a large number of files on local disks, the web, or a number of different shared file systems. In order to analyze this data, the user can chose out of a wide set of mathematical and statistical functions, including linear algebra classes, numerical algorithms such as integration and minimization, and various methods for performing regression analysis (fitting). In particular, ROOT offers packages for complex data modeling and fitting, as well as multivariate classification based on machine learning techniques. A central piece in these analysis tools are the histogram classes which provide binning of one- and multi-dimensional data. Results can be saved in high-quality graphical formats like Postscript and PDF or in bitmap formats like JPG or GIF. The result can also be stored into ROOT macros that allow a full recreation and rework of the graphics. Users typically create their analysis macros step by step, making use of the interactive C++ interpreter CINT, while running over small data samples. Once the development is finished, they can run these macros at full compiled speed over large data sets, using on-the-fly compilation, or by creating a stand-alone batch program. Finally, if processing farms are available, the user can reduce the execution time of intrinsically parallel tasks - e.g. data mining in HEP - by using PROOF, which will take care of optimally distributing the work over the available resources in a transparent way

Investigating distributed simulation at the Ford motor company

Engine production is a complex process that requires the manufacturing and assembly of a wide variety of components to create a varied product mix. Simulation plays a key role in the planning process of a new production line to determine if it can meet expected demand. However, these simulations can be very time consuming and can often take up to a day to execute a single run. This paper investigates how distributed simulation based on the IEEE 1516 High Level Architecture and the emerging standard COTS Simulation Package Interoperability Product Development Group (CSPI-PDG) Type I Interoperability Reference Model could be used to reduce the time taken for a single simulation run. CSP interoperability and the problem of integrating CSPs with HLA software (the runtime infrastructure) are presented. New prototype benchmarking software, the COTS Simulation Package Emulator (CSPE), which is being developed to investigate distributed simulation problems, is discussed. The paper then develops a case study of how this was used to investigate the feasibility of using distributed simulation at Ford. The paper discusses results obtained from this case study and suggests that distributed simulation could indeed be beneficial to Ford