In solving the foward problem one runs a model (based on the physical laws that govern the system) to predict (observations of) reality. In solving the inverse problem one uses observations of reality to infer the properties of the model. Inverse problems are tremendously important in many fields from biology to nuclear engineering to numerical weather prediction. They are challenging because the non-uniqueness of the solution and ill-conditioning.
This class introduces computational methods for solving inverse problems constrained by differential equations. Topics discussed in the course include:
For detailed information please consult the syllabus ( PDF).
The final grade is based on homework projects.
Project 1: Ensemble Kalman filters.
Project 2: Particle filters.
Project 3. 4D-Var.
firstname.lastname@example.org (Adrian Sandu)