Intelligent automated surface grid generation

The goal of our research is to produce a flexible, general grid generator for automated use by other programs, such as numerical optimizers. The current trend in the gridding field is toward interactive gridding. Interactive gridding more readily taps into the spatial reasoning abilities of the human user through the use of a graphical interface with a mouse. However, a sometimes fruitful approach to generating new designs is to apply an optimizer with shape modification operators to improve an initial design. In order for this approach to be useful, the optimizer must be able to automatically grid and evaluate the candidate designs. This paper describes and intelligent gridder that is capable of analyzing the topology of the spatial domain and predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains of wide range of configurations

Distributed and Interactive Simulations Operating at Large Scale for Transcontinental Experimentation

This paper addresses the use of emerging technologies to respond to the increasing needs for larger and more sophisticated agent-based simulations of urban areas. The U.S. Joint Forces Command has found it useful to seek out and apply technologies largely developed for academic research in the physical sciences. The use of these techniques in transcontinentally distributed, interactive experimentation has been shown to be effective and stable and the analyses of the data find parallels in the behavioral sciences. The authors relate their decade and a half experience in implementing high performance computing hardware, software and user inter-face architectures. These have enabled heretofore unachievable results. They focus on three advances: the use of general purpose graphics processing units as computing accelerators, the efficiencies derived from implementing interest managed routers in distributed systems, and the benefits of effective data management for the voluminous information

P4ML Evaluation Results, Apr2018

<p>Evaluation results for the P4ML system. There are four files:</p> <ul> <li>DSBox-reults-all: contains the results of the p4ml system</li> <li>sklearn-results.txt: contains the results of the auto-sklearn system</li> <li>join-results.csv: p4ml results and autosklearn results aggregated together</li> <li>processing.txt: script used to count which system was better in each case, according to the current performance metric</li> </ul> <p> </p

Intelligent Automated Grid Generation for Numerical Simulations

Numerical simulation of partial differential equations (PDEs) plays a crucial role in predicting the behavior of physical systems and in modern engineering design. However, in order to produce reliable results with a PDE simulator, a human expert must typically expend considerable time and effort in setting up the simulation. Most of this effort is spent in generating the grid, the discretization of the spatial domain which the PDE simulator requires as input. To properly design a grid, the gridder must not only consider the characteristics of the spatial domain, but also the physics of the situation and the peculiarities of the numerical simulator. This paper describes an intelligent gridder that is capable of analyzing the topology of the spatial domain and predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically gridding programs are given a partitioning of the spatial..

Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) 2005 Simulation Data Grid: Joint Experimentation Data Management and Analysis

The need to present quantifiable results from simulations to support transformational findings is driving the creation of very large and geographically dispersed data collections. The Joint Experimentation Directorate (J9) of USJFCOM and the Joint Advanced Warfighting Project is conducting a series of Urban Resolve experiments to investigate concepts for applying future technologies to joint urban warfare. The recently concluded phase I of the experiment utilized and integrated multiple scalable parallel processors (SPP) sites distributed across the United States from supercomputing centers at Maui and at Wright-Patterson to J9 at Norfolk, Virginia. This computational power is required to model futuristic sensor technology and the complexity of urban environments. For phase I the simulation generated more than two terabytes of raw data at rate of&gt;10GB per hour. The size and distributed nature of this type of data collection pose significant challenges in developing the corresponding data-intensive applications that manage and analyze them. Building on lessons learned in developing data management tools for Urban Resolve, we present our next generation data management and analysis tool, called Simulation Data Grid (SDG). The design principles driving the design of SDG are 1) minimize network communication overhead (especially across SPPs) by storing data near the point of generation and only selectively propagating the data as needed, and 2) maximize the use of SPP computational resources and storage by distributing analyses across SPP sites to reduce, filter and aggregate. Our key implementation principle is to leverage existing open standards and infrastructure from Grid Computing. We show how our services interface and build on top o

Large-Scale Simulation Experimentation and Analysis: Database Programming Using Java

High Performance Computing has made significant strides in the distributed simulation community. The Joint Forces Command has fielded more than a million independent agents in its JESPP project, with a concomitant data management challenge. Enabling and optimizing this transcontinental computing and analysis environment has drawn significant interest from the T&amp;E community. This paper focuses on the Scalable Data Grid (SDG) project at the Information Sciences Institute and it illuminates why some Java techniques were found to be useful and some were not. The study of the programming will aid in examining the design and implementation of an effective distributed simulation database using the Java Programming Language and its associated tools and Application Programming Interfaces. The transition of intelligent agent simulations from training to experimentation requires the effectual logging, processing, storing, retrieving, and analyzing terabytes of data. The design, construction, and evaluation of the SDG strives to balance efficiency of execution, clarity of development, and security of the environment to create a robust, scalable system to support distributed simulation database population, organization, and utilization. The choice of the most appropriate programming language was central to the effective development and eventual utility of the SDG. The depth and breadth of Java technologies provide a rich se

Dynamic Topology Reconfiguration of Boltzmann Machines on Quantum Annealers

Boltzmann machines have useful roles in deep learning applications, such as generative data modeling, initializing weights for other types of networks, or extracting efficient representations from high-dimensional data. Most Boltzmann machines use restricted topologies that exclude looping connectivity, as such connectivity creates complex distributions that are difficult to sample. We have used an open-system quantum annealer to sample from complex distributions and implement Boltzmann machines with looping connectivity. Further, we have created policies mapping Boltzmann machine variables to the quantum bits of an annealer. These policies, based on correlation and entropy metrics, dynamically reconfigure the topology of Boltzmann machines during training and improve performance