1- SEEDS

Understanding and predicting the dynamics of complex systems is a key task in diverse areas, including biology, epidemics, engineering and economics. However, devising sufficiently accurate models for real systems remains a challenging task. Structural model errors caused by insufficient knowledge about the quantitative interactions in the real system and hidden inputs from the environment are fundamental obstacles to model development. In this project, we devise methods and algorithms for reconstructing the root cause of these model errors from data, thereby facilitating the systematic extension and improvement of models. We have recently devised some new criteria for the unique recovery of the model error signal from output measurements. Based on this progress, we aim at designing more robust recovery algorithms with proven guarantees for the accuracy of the reconstruction. As a second main research goal, we will investigate the automatic extension of incomplete models by discovering the governing equations missing in this model.