SIAM/AMEP Seminar

Mathematically speaking, a DT can often be described as a partially observed Markov decision process (POMDP). Solving POMDPs computationally constitutes one of the most challenging problems around. Still, tremendous progress has been made in closely related fields such as data assimilation, uncertainty quantification, control and optimisation, and model reduction. A key emerging question is thus how we can successfully synthesise these advances into a tool broadly applicable to DT.