IIHR—Hydroscience and Engineering is hosting a Short Series Lecture on Dynamic Data Assimilation by Dr. S. Lakshmivarahan, organized by Humberto Vergara for the research community of the College of Engineering at the University of Iowa.
The workshop is scheduled for Tuesday, March 12, and Wednesday, March 13, 2024, in Seminar Room 127 of the Stanley Hydraulics Lab. This guest lecture is available free of cost to all IIHR and CoE students, faculty, and staff. Lunch will be provided on both days for those who register.
There will be three 1-hour lectures each day, for a total of six lectures in all, covering the following topics:
- Data Science – Data Mining, Data Assimilation, and Prediction – a global view with examples
- State-space formulation of dynamic models in discrete time – Dependence of solution on initial and boundary conditions and parameters
- Data Assimilation using four-dimensional variational method (4-D VAR), First-order adjoint formulation. Computation of adjoint gradient and minimization algorithm
- Derivation of the dynamic equations for the evolution of sensitivity of model solution to initial conditions and parameters – forward sensitivity method (FSM)
- Reformulation of dynamic data assimilation using FSM and the relation between 4-D VAR and FSM
- Introduction to observability and derivation of observability Gramian
- Placement of observations and positive definiteness of observability Gramian
- Introduction to Kalman filtering and ensemble methods
Course schedule:
Tuesday, March 12, 2024
09:35 am – 09:45 am Set-up and Introductions
09:45 am – 10:45 am Lecture 1 session
11:00 am – 12:00 pm Lecture 2 session
12:30 pm – 1:50 pm Catered lunch
02:00 pm – 03:00 pm Lecture 3 session
Wednesday, March 13, 2024
11:00 am – 12:00 pm Lecture 4 session
12:30 pm – 1:50 pm Catered lunch
02:00 pm – 03:00 pm Lecture 5 session
03:15 pm – 04:15 pm Lecture 6 session
Professor Lakshmivarahan is the George Lynn Cross Research Professor Emeritus in the School of Computer Science at the University of Oklahoma. He has won numerous awards for both research and teaching related to his research interests in data mining and analytics, data assimilation, computational finance, parallel computation, and learning algorithms.