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Triangular mesh of domain Ω.

Triangular mesh of domain Ω.

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Figure 2-2: One cycle of the online stage of the Dynamic Sensor...
Figure 2-3: Trade-off between full and reduced-order model in one...
Figure 2-4: Comparison between inverse and forward problem.
Figure 3-1: Triangular mesh of domain Ω.
+9 Figure 3-2: This figure depicts the sample test initial condition used...
Model Reduction for Dynamic Sensor Steering: A Bayesian Approach to Inverse Problems
Article
Full-text available
  • Aug 2010
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 97-101). In many settings, distributed sensors provide...
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An efficient goal based reduced order model approach for targeted adaptive observations: An efficient goal based reduced order model approach for targeted adaptive observations
Article
  • Jul 2016
  • INT J NUMER METH FL
An efficient adjoint sensitivity technique for optimally collecting targeted observations is presented. The targeting technique incorporates dynamical information from the numerical model predictions to identify when, where and what types of observations would provide the greatest improvement to specific model forecasts at a future time. A functional (goal) is defined to measure what is considered important in modelling problems. The adjoint sensitivity technique is used to identify the impact of observations on the predictive accuracy of the functional, then placing the sensors at the locations with high impacts. The adaptive (goal) observation technique developed here has the following features: (i) over existing targeted observation techniques, its novelty lies in that the interpolation error of numerical results is introduced to the functional (goal), which ensures the measurements are a distance apart; (ii) the use of proper orthogonal decomposition (POD) and reduced order modelling for both the forward and backward simulations, thus reducing the computational cost; and (iii) the use of unstructured meshes. The targeted adaptive observation technique is developed here within an unstructured mesh finite element model (Fluidity). In this work, a POD reduced order modelling is used to form the reduced order forward model by projecting the original complex model from a high dimensional space onto a reduced order space. The reduced order adjoint model is then constructed directly from the reduced order forward model. This efficient adaptive observation technique has been validated with two test cases: a model of an ocean gyre and a model of 2D urban street canyon flows. Copyright © 2016 John Wiley & Sons, Ltd.
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