Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust for smaller training sets, derive better generalization problems, and are faster to train. In this paper, we show that using PINNs in comparison with purely data-driven neural networks is not only favorable for training performance but allows us to extract significant information on the quality of the approximated solution. Assuming that the underlying differential equation for the PINN training is an ordinary differential equation, we derive a rigorous upper limit on the PINN prediction error. This bound is applicable even for input data not included in the training phase and without any prior knowledge about the true solution. Therefore, our a posteriori error estimation is an essential step to certify the PINN. We apply our error estimator exemplarily to two academic toy problems, whereof one falls in the category of model-predictive control and thereby shows the practical use of the derived results.
%0 Conference Paper
%1 9892569
%A Hillebrecht, Birgit
%A Unger, Benjamin
%B 2022 International Joint Conference on Neural Networks (IJCNN)
%D 2022
%K EXC2075 PN4 SimTech from:bhillebrecht myown peerReviewed
%P 1-8
%R 10.1109/IJCNN55064.2022.9892569
%T Certified machine learning: A posteriori error estimation for physics-informed neural networks
%X Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust for smaller training sets, derive better generalization problems, and are faster to train. In this paper, we show that using PINNs in comparison with purely data-driven neural networks is not only favorable for training performance but allows us to extract significant information on the quality of the approximated solution. Assuming that the underlying differential equation for the PINN training is an ordinary differential equation, we derive a rigorous upper limit on the PINN prediction error. This bound is applicable even for input data not included in the training phase and without any prior knowledge about the true solution. Therefore, our a posteriori error estimation is an essential step to certify the PINN. We apply our error estimator exemplarily to two academic toy problems, whereof one falls in the category of model-predictive control and thereby shows the practical use of the derived results.
@inproceedings{9892569,
abstract = {Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust for smaller training sets, derive better generalization problems, and are faster to train. In this paper, we show that using PINNs in comparison with purely data-driven neural networks is not only favorable for training performance but allows us to extract significant information on the quality of the approximated solution. Assuming that the underlying differential equation for the PINN training is an ordinary differential equation, we derive a rigorous upper limit on the PINN prediction error. This bound is applicable even for input data not included in the training phase and without any prior knowledge about the true solution. Therefore, our a posteriori error estimation is an essential step to certify the PINN. We apply our error estimator exemplarily to two academic toy problems, whereof one falls in the category of model-predictive control and thereby shows the practical use of the derived results.},
added-at = {2022-10-25T16:29:09.000+0200},
author = {Hillebrecht, Birgit and Unger, Benjamin},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/23de1efd3564df7071a668a7e5c4039bb/bhillebrecht},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
doi = {10.1109/IJCNN55064.2022.9892569},
interhash = {4304a67819c757c85c5b19e1eec97200},
intrahash = {3de1efd3564df7071a668a7e5c4039bb},
issn = {2161-4407},
keywords = {EXC2075 PN4 SimTech from:bhillebrecht myown peerReviewed},
month = {July},
pages = {1-8},
timestamp = {2022-10-25T14:42:36.000+0200},
title = {Certified machine learning: A posteriori error estimation for physics-informed neural networks},
year = 2022
}
