Repository: Freie Universit├Ąt Berlin, Math Department

Ensemble Kalman filter for neural network based one-shot inversion

Guth, Philipp A. and Schillings, Claudia and Weissmann, Simon (2020) Ensemble Kalman filter for neural network based one-shot inversion. . . (Submitted)

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Official URL: https://doi.org/10.48550/arXiv.2005.02039

Abstract

We study the use of novel techniques arising in machine learning for inverse problems. Our approach replaces the complex forward model by a neural network, which is trained simultaneously in a one-shot sense when estimating the unknown parameters from data, i.e. the neural network is trained only for the unknown parameter. By establishing a link to the Bayesian approach to inverse problems, an algorithmic framework is developed which ensures the feasibility of the parameter estimate w.r. to the forward model. We propose an efficient, derivative-free optimization method based on variants of the ensemble Kalman inversion. Numerical experiments show that the ensemble Kalman filter for neural network based one-shot inversion is a promising direction combining optimization and machine learning techniques for inverse problems.

Item Type:Article
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
ID Code:2833
Deposited By: Ulrike Eickers
Deposited On:04 May 2022 06:18
Last Modified:04 May 2022 06:18

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