ModLEARN
Structured model learning
The goal of this subproject is to develop and analyze a machine-learning framework that allows to augment approximately correct physical models with components that are learned from data. This is in particular important for MRI, where existing physical models that describe the measurement process are not always capable of modelling all involved processes sufficiently well.
Our subproject will provide new possibilities of learning physical models from data in a highly structured way. Within our project, we will answer question such as: How much data do I need to learn components of a model with a certain complexity? Is it possible to uniquely identify the underlying physics from the available data? How do I implement machine learning algorithms for model learning in a way that the correct model can be found?
By answering these questions, our subproject aims to contribute to a better understanding of the of different MR imaging protocols and, ultimately, to provide improved imaging techniques for medical diagnosis and therapy control.
In our project, we will collaborate with IdCONTROL on all-at-once approaches for the Bloch-Torrey equation and extensions, with OptOP on optimal operator learning for augmented, non-linear models, with PriorLEARN on high-precision priors for physical parameters to reduce ambiguities to a minimum, and with GenMRI on specific measurements tailored to learning structured models.