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2016 SIAM - Society for Industrial and Applied Mathematics

Dynamic Data Driven Applications Systems (DDDAS) is a paradigm whereby the computation and instrumentation aspects of a system are dynamically integrated in a feedback control loop. Sensor data can be dynamically incorporated to affect the system's operation, and in reverse, the executing model can control the instrumentation. The minisymposium presents advances in DDDAS methods, which draw on fields such as sensor fusion, reduced modeling, inverse problems, data assimilation, uncertainty quantification, and decision support.

Organizers: Karen E. Willcox Massachusetts Institute of Technology, USA, Kamran Mohseni University of Florida, USA

MS41 Dynamic Data Driven Applications Systems - Part I of II

  • Proper Symplectic Decomposition for Model Reduction of Forced and Dissipative Hamiltonian System
    • Liqian Peng and Kamran Mohseni, University of Florida, USA
  • Dynamic Data-Driven Decisions for Real-Time Adaptive Aircraft Path Planning
    • Victor Singh and Karen E. Willcox, Massachusetts Institute of Technology, USA
  • A Closed-Loop Context Aware Data Acquisition and Resource Allocation Framework for Dynamic Data Driven Applications Systems (DDDAS) on the Cloud
    • Mohammad Maifi Hasan Khan, University of Connecticut, USA
  • A Non-Parametric Framework for Inference Using Dynamically Deformed and Targeted Manifolds
    • Sai Ravela, Massachusetts Institute of Technology, USA

MS56 Dynamic Data Driven Applications Systems - Part II of II