Event Information:

• Thu

  15

  Jan

  2026

  SC Seminar: André Gustavo Carlon

  10:15Hybrid (Room 32-349 and via Zoom)

  Dr. André Gustavo Carlon, Chair of Mathematics for Uncertainty Quantification, RWTH Aachen University

  Title: Bayesian optimal experimental design and its applications in engineering
  

  Abstract:

  Bayesian optimal experimental design (OED) seeks to optimize data acquisition by maximizing the expected information gain (EIG). In nonlinear problems, however, estimating and optimizing the EIG is computationally demanding, often requiring a prohibitively large number of model evaluations. In this talk, I show how Laplace-based approximations can be used to make Bayesian OED tractable in challenging engineering applications.

  First, I present a stochastic gradient descent (SGD) approach in which the Laplace approximation is used to obtain noisy but inexpensive gradient estimates of the EIG. The robustness of SGD to noise enables efficient optimization of experimental designs using coarse EIG approximations. I demonstrate the method in an electrical impedance tomography experiment aimed at identifying ply orientation angles in composite laminate materials.

  In the second application, I consider a source localization problem using unmanned aerial vehicles (UAVs), where the goal is to identify the source of a pollutant from concentration measurements. The optimal UAV path is obtained by solving a mixed discrete–continuous stochastic optimal control problem governed by a Hamilton–Jacobi–Bellman equation, with a value function defined in terms of the EIG. To address the high dimensionality of the resulting PDE, I again employ a Laplace approximation of the EIG.

  How to join online

  You can join online via Zoom, using the following link:
  https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1