Dr. André Gustavo Carlon
(Chair of Mathematics for Uncertainty Quantification, RWTH Aachen University)hosted by Seminar Series on Scientific Computing
"Bayesian optimal experimental design and its applications in engineering"
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.
| Time: | Thursday, 15.01.2026, 10:15 |
|---|---|
| Place: | 32-349 |
| Video: | https://uni-kl-de.zoom-x.de/j/69269239534?pwd=Z9UOzMpkhMjrxVhll3d49sNHFe9Fd1.1 |
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