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Sunday, 15 November 2020 12:00

Bayesian Optimization (Part 2)

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  • Abstract This is the second blog of a series that introduces the concept oy Bayesian optimization (BO). In the first part, we have introduced Gaussian processes as a means to predict the behaviour of the objective function for unkown parameter values. In this second part the BO algorithm is introduced, which uses these predictions to find promising parameter values to sample the expensive objective function. Finally, the performance of BO is compared to other optimization methods showing a great perfomance gain.
Sunday, 01 November 2020 12:00

Bayesian Optimization (Part 1)

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  • Abstract This is the first blog of a series that introduces the concept oy Bayesian optimization (BO). BO uses a stochastic model of the objective function in order to find promising parameter values. The most commonly applied model is a Guassian process. The first part of the series explains Gaussian processes and Gaussian process regression.

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  • Abstract JCMsuite employs the finite element method (FEM) in order to simulate the electrodynamic, mechanic and thermodynamic behavior of nano-optical systems. In this blog we want to motivate and describe the usage FEM for determining the electrodynamic system properties. We will compare the method to other common approaches like the rigorous coupled-wave analysis (RCWA) and the finite difference time domain (FDTD). Finally, we will present some benchmarks to show that FEM can be orders of magnitude faster and more precise than alternative methods.
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