Numerical Problems --- linear algebra and optimization, integration and the solution of differential equations --- are the computational bottleneck of artificial intelligent systems. Intriguingly, the numerical algorithms used for these tasks are also compact little intelligent agents themselves. They estimate unknown / uncomputable quantities by observing the result of feasible computations. They also actively decide which computations to perform.
The Research Group on Probabilistic Numerics studies this philosophical and mathematical connection between computation and inference. We aim to build a theoretical understanding of numerical computer algorithms as agents acting rationally under uncertainty. We analyse existing algorithms from this viewpoint, and propose novel algorithms that provide functionality for key computational challenges in the science of Intelligent Systems.
From early 2015 until late 2016, our work was kindly supported by a grant in the Emmy Noether Programme of the DFG. From Dezember 2016, the group is principally funded by the Max Planck Society.