13,979 research outputs found

    21st Century Simulation: Exploiting High Performance Computing and Data Analysis

    Get PDF
    This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

    Agent-Based Modeling: The Right Mathematics for the Social Sciences?

    Get PDF
    This study provides a basic introduction to agent-based modeling (ABM) as a powerful blend of classical and constructive mathematics, with a primary focus on its applicability for social science research.� The typical goals of ABM social science researchers are discussed along with the culture-dish nature of their computer experiments. The applicability of ABM for science more generally is also considered, with special attention to physics. Finally, two distinct types of ABM applications are summarized in order to illustrate concretely the duality of ABM: Real-world systems can not only be simulated with verisimilitude using ABM; they can also be efficiently and robustly designed and constructed on the basis of ABM principles. �

    Construals as a complement to intelligent tutoring systems in medical education

    Get PDF
    This is a preliminary version of a report prepared by Meurig and Will Beynon in conjunction with a poster paper "Mediating Intelligence through Observation, Dependency and Agency in Making Construals of Malaria" at the 11th International Conference on Intelligent Tutoring Systems (ITS 2012) and a paper "Construals to Support Exploratory and Collaborative Learning in Medicine" at the associated workshop on Intelligent Support for Exploratory Environments (ISEE 2012). A final version of the report will be published at a later stage after feedback from presentations at these events has been taken into account, and the experimental versions of the JS-EDEN interpreter used in making construals have been developed to a more mature and stable form

    Agent-based modeling: a systematic assessment of use cases and requirements for enhancing pharmaceutical research and development productivity.

    Get PDF
    A crisis continues to brew within the pharmaceutical research and development (R&D) enterprise: productivity continues declining as costs rise, despite ongoing, often dramatic scientific and technical advances. To reverse this trend, we offer various suggestions for both the expansion and broader adoption of modeling and simulation (M&S) methods. We suggest strategies and scenarios intended to enable new M&S use cases that directly engage R&D knowledge generation and build actionable mechanistic insight, thereby opening the door to enhanced productivity. What M&S requirements must be satisfied to access and open the door, and begin reversing the productivity decline? Can current methods and tools fulfill the requirements, or are new methods necessary? We draw on the relevant, recent literature to provide and explore answers. In so doing, we identify essential, key roles for agent-based and other methods. We assemble a list of requirements necessary for M&S to meet the diverse needs distilled from a collection of research, review, and opinion articles. We argue that to realize its full potential, M&S should be actualized within a larger information technology framework--a dynamic knowledge repository--wherein models of various types execute, evolve, and increase in accuracy over time. We offer some details of the issues that must be addressed for such a repository to accrue the capabilities needed to reverse the productivity decline

    Multi-Scale Modeling of the Innate Immune System: A Dynamic Investigation into Pathogenic Detection

    Get PDF
    Having a well-functioning immune system can mean the difference between a mild ailment and a life-threatening infection; however, predicting how a disease will progress has proven to be a significant challenge. The dynamics driving the immune system are governed by a complex web of cell types, signaling proteins, and regulatory genes that have to strike a balance between disease elimination and rampant inflammation. An insufficient immune response will induce a prolonged disease state, but an excessive response will cause unnecessary cell dead and extensive tissue damage. This balance is usually self-regulated, but medical intervention is often necessary to correct imbalances. Unfortunately, these therapies are imperfect and accompanied by mild to debilitating side-effects caused by off-target effects. By developing a detailed understanding of the immune response, the goal of this dissertation is to predict how the immune system will respond to infection and determine how new potential therapies could overcome these threats. Computational modeling provides an opportunity to synthesize current immunological observations and predict response outcomes to pathogenic infections. When coupled with experimental data, these models can simulate signaling pathway dynamics that drive the immune response, incorporate regulatory feedback mechanisms, and model inherent biological noise. Taken together, computational modeling can explain emergent behavior that cannot be determined from experiment alone. This dissertation will unitize two computational modeling techniques: ordinary differential equations (ODEs) and agent-based modeling (ABMs). Ultimately, they are combined in a novel way to model cellular immune responses across multiple length scales, creating a more accurate representation of the pathogenic response. TLR4 and cGAS signaling are prominent in a number of diseases and dysregulations including---but not limited to---autoimmunity, cancer, HIV, HSV, tuberculosis, and sepsis. These two signaling pathways are so prevalent because they are activated extremely early and help drive the downstream immune signaling. Modeling how cells dynamically regulate these pathways is critical for understanding how diseases circumvent feedback mechanisms and how new therapies can restore immune function to combat disease progression. By using ODE and ABM techniques, these studies aim to incrementally expand our knowledge of innate immune signaling and understand how feedback mechanisms control disease severity
    corecore