Capturing Scientists’ Insight for DDDAS

  • Paul Reynolds
  • David Brogan
  • Joseph Carnahan
  • Yannick Loitière
  • Michael Spiegel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)


One of the intended consequences of utilizing simulations in dynamic, data-driven application systems is that the simulations will adjust to new data as it arrives. These adjustments will be difficult because of the unpredictable nature of the world and because simulations are so carefully tuned to model specific operating conditions. Accommodating new data may require adapting or replacing numerical methods, simulation parameters, or the analytical scientific models from which the simulation is derived. In this research, we emphasize the important role a scientist’s insight can play in facilitating the runtime adaptation of a simulation to accurately utilize new data. We present the tools that serve to capture and apply a scientist’s insight about opportunities for, and limitations of, simulation adaptation. Additionaly, we report on the two ongoing collaborations that serve to guide and evaluate our research.


Parton Distribution Function Subject Matter Expert Language Construct Parton Distribution Function Model Abstraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paul Reynolds
    • 1
  • David Brogan
    • 1
  • Joseph Carnahan
    • 1
  • Yannick Loitière
    • 1
  • Michael Spiegel
    • 1
  1. 1.Computer Science DepartmentUniversity of Virginia 

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