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. ArXiv . (Submitted)

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

Abstract

We studythe useofnoveltechniquesarisinginmachinelearningforinverse 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 parametersfromdata, ie, theneuralnetworkistrainedonlyfortheunknownparameter. By establishing a link to the Bayesian approach to inverse problems we develop an algorithmic framework that ensures the feasibility of the parameter estimate with respect 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
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics > Deterministic and Stochastic PDEs Group
ID Code:2984
Deposited By: Ulrike Eickers
Deposited On:25 May 2023 13:47
Last Modified:25 May 2023 13:47

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