Matthias Chung
Mathematical modeling has been a key tool in various scientific fields (such as biology, medicine, and engineering) in understanding systems dynamics. Parameter inference for such systems have been established to quantify the underlying dynamics and ultimately make informed prediction. Here, parameter estimation techniques heavily rely on computational methods to provide robust estimates and predictable uncertainties.
However, ill-posedness, due to noisy data, complex systems, and model inaccuracies cause novel challenges and new methods need to be established. In this talk we will discuss new computational methods for parameter and uncertainty estimation for dynamical systems as well es discuss novel techniques for optimal experimental design. We illustrate these methods on examples from biological systems and medical imaging.
Invited by Jana de Wiljes
