Optimization with learning-informed differential equation constraints and its applications

Inspired by applications in optimal control of semilinear elliptic partial differential equations and physics-integrated imaging, differential equation constrained optimization problems with constituents that are only accessible through data-driven techniques are studied. A particular focus is on the analysis and on numerical methods for problems with machine-learned components. For a rather general context, an error analysis is provided, and particular properties resulting from artificial neural network based approximations are addressed. Moreover, for each of the two inspiring applications analytical details are presented and numerical results are provided.

Keywords

PDE constrained optimization, artificial neural network, semilinear PDEs, integrated physicsbased, imaging, learning-informed model, quantitative MRI, semi-smooth Newton SQP algorithm

URI

https://oa.tib.eu/renate/handle/123456789/9404
https://doi.org/10.34657/8442

Citation

Dong, G., Hintermüller, M., & Papafitsoros, K. (2020). Optimization with learning-informed differential equation constraints and its applications (Vol. 2754). Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik. https://doi.org//10.20347/WIAS.PREPRINT.2754

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Mathematik
WIAS Preprints

License

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