# Using Gaussian process regression for efficient parameter reconstruction

arXiv: https://arxiv.org/abs/1903.12128 · HTML: https://mlsanity.com/paper/1903.12128/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2019.

## Built on by (most cited first, 1 of 1)

- [Impact Study of Numerical Discretization Accuracy on Parameter Reconstructions and Model Parameter Distributions](https://mlsanity.com/paper/2305.02663/) (2023)

## Abstract (arXiv)

Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian optimization to different local minimization algorithms for this numerical optimization problem. Bayesian optimization uses Gaussian-process regression to find promising parameter values. We examine how pre-computed simulation results can be used to train the Gaussian process and to accelerate the optimization.