Studies of inverse problems using sequential methods

My presentation is set within the framework of inverse problems. The main objective is to determine initial conditions, the state, or parameters of a system from available observations, with a particular focus on biological applications. We concentrate on sequential methods in data assimilation, where observations are incorporated as they become available. In this context, I present two examples: the reconstruction of a source term in a wave equation, and the determination of both state and parameters in a PDE system modeling tumor growth. For the first problem, we define a Kalman estimator in infinite dimensions that sequentially estimates the source term. We show that this sequential estimator is equivalent to minimizing a functional, which allows us to perform convergence analysis under observability conditions. The second project studies the evolution of non-spherical tumor growth by combining mathematical modeling with data assimilation from biological measurements. The general strategy is to extract relevant information from images of spheroids, formulate a PDE model for tumor evolution, and then reduce it to an ODE model. A reduced ROUKF coupled with a Luenberger observer is then used to estimate both the state and the parameters.