Gaussian process optimization with simulation failures


Event details

Date 27.02.2019
Hour 11:0012:00
Speaker Dr. François Bachoc
Category Conferences - Seminars

We address the optimization of a computer model, where each simulation either fails or returns a valid output performance. We suggest a joint Gaussian process model for classification of the inputs (computation failure or success) and for regression of the performance function. We discuss the maximum likelihood estimation of the covariance parameters, with a stochastic approximation of the gradient. We then extend the celebrated expected improvement criterion to our setting of joint classification and regression, thus obtaining a global optimization algorithm. We prove the convergence of this algorithm. We also study its practical performances, on simulated data, and on a real computer model in the context of automotive fan design.


Practical information

  • Informed public
  • Free


  • Idiap Research Institute



computer AI

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