Merge branch '1324-machine-learning-methods-for-parallel-in-time-algorithms' into 'dev'

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

Closes #1324

See merge request !172

topics/Machine_Learning_For_PINT.md

+# 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)