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Commit b3f3a6c9 authored by Fabian Nuraddin Alexander Gabel's avatar Fabian Nuraddin Alexander Gabel :speech_balloon:
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Merge branch '1324-machine-learning-methods-for-parallel-in-time-algorithms' into 'dev'

Resolve "Machine Learning methods for Parallel-in-time algorithms"

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4 merge requests!197Revert "remove math from title",!172Resolve "Machine Learning methods for Parallel-in-time algorithms",!171updating names,!170updating names
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# Machine Learning methods for Parallel-in-time algorithms
### Working Groups: cm
### Collaborators (MAT): aibrahim, druprecht, sgoetschel
## Description
We explore the use of machine learning based coarse propagators in the Parareal parallel-in-time algorithm. The aim is to solve nonlinear time-dependent partial differential equations faster. We consider, as an example, the time-dependent nonlinear Black-Scholes equation, which may be used to value financial options and to calculate implied volatilities. We will show that that an ML-based coarse propagator can lead to faster Parareal convergence. Faster convergence would mean better speedup which, in turn, could help to build financial analysis tools that enable traders to make a rapid and systematic evaluation of buy/sell contracts.
## Publications and Manuscripts
[1] [Debia Wakhloo, Scharkowski, Franziska, Abdul Qadir Ibrahim et al., Functional hypoxia drives neuroplasticity and neurogenesis via brain erythropoietin, *Nature Communications* 11, Article number: 1313 (2020), 2020.](https://doi.org/10.1038/s41467-020-15041-1)
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