Archive for Rational Mechanics and Analysis

, Volume 216, Issue 3, pp 921–981 | Cite as

Optimal Shape and Location of Sensors for Parabolic Equations with Random Initial Data

  • Yannick Privat
  • Emmanuel Trélat
  • Enrique Zuazua


In this article, we consider parabolic equations on a bounded open connected subset \({\Omega}\) of \({\mathbb{R}^n}\). We model and investigate the problem of optimal shape and location of the observation domain having a prescribed measure. This problem is motivated by the question of knowing how to shape and place sensors in some domain in order to maximize the quality of the observation: for instance, what is the optimal location and shape of a thermometer? We show that it is relevant to consider a spectral optimal design problem corresponding to an average of the classical observability inequality over random initial data, where the unknown ranges over the set of all possible measurable subsets of \({\Omega}\) of fixed measure. We prove that, under appropriate sufficient spectral assumptions, this optimal design problem has a unique solution, depending only on a finite number of modes, and that the optimal domain is semi-analytic and thus has a finite number of connected components. This result is in strong contrast with hyperbolic conservative equations (wave and Schrödinger) studied in Privat et al. (J Eur Math Soc, 2015) for which relaxation does occur. We also provide examples of applications to anomalous diffusion or to the Stokes equations. In the case where the underlying operator is any positive (possible fractional) power of the negative of the Dirichlet-Laplacian, we show that, surprisingly enough, the complexity of the optimal domain may strongly depend on both the geometry of the domain and on the positive power. The results are illustrated with several numerical simulations.


Quantum Limit Measurable Subset Optimal Design Problem Optimal Domain Hilbert Basis 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yannick Privat
    • 1
  • Emmanuel Trélat
    • 2
  • Enrique Zuazua
    • 3
    • 4
  1. 1.CNRS, Sorbonne Universités, UPMC Univ Paris 06, UMR 7598Laboratoire Jacques-Louis LionsParisFrance
  2. 2.Sorbonne Universités, UPMC Univ Paris 06, CNRS UMR 7598Laboratoire Jacques-Louis Lions, Institut Universitaire de FranceParisFrance
  3. 3.Ikerbasque, Basque Foundation for ScienceBilbaoSpain
  4. 4.BCAM, Basque Center for Applied MathematicsBilbaoSpain

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