|Description:||We consider the inverse problem of discovering the location of point sources from very sparse point measurements in a bounded domain that contains impenetrable obstacles. The sources spread according to a class of linear equations, including the Laplace, heat, and Helmholtz equations, and limited information of the obstacles may only be estimated using the visibility from measurement location.
For these settings, we present a greedy algorithm for source discovery. The algorithm adaptively adds new measurement locations in order to improve estimates of the sources.
|Speaker:||Richard Tsai (Univ. Texas at Austin, USA)|
|Institution:||University of Texas at Austin|