Content: A digital twin (DT) is a computational model that evolves over time to persistently represent the structure, behavior, and context of a unique physical system or process. DTs are characterized by a dynamic and continuous two-way flow of information between the computational model and the physical system. Data streams from the physical system are assimilated into the computational model to reduce uncertainties and improve predictions of the model, which in turn is used as a basis for controlling the physical system, optimizing data acquisition, and providing decision support. From a mathematical perspective, digital twins naturally lead to problems in stochastic modeling, data assimilation, filtering, and optimal control. The DT paradigm therefore raises challenging mathematical and statistical questions which will be at the center of the seminar.

Target audience: M.Sc. Mathematik/Physik, BMS course

Requirements: Stochastic I, II and Numerics I, II.