EF1 – Extracting dynamical Laws from Complex Data

We propose combining recent advances in the computation of conditional (posterior probability) distributions via Hilbert space embedding with the stochastic analysis of partially observed dynamical systems —exemplified by ensemble Kalman methods— to develop, analyse, and apply novel learning methods for profoundly nonlinear, multimodal problems.