Learn · Dynamic Data Assimilation: an introduction
Lecture 9 of 40Deterministic, Static Inverse Problems
Deterministic, Static, linear Inverse (well-posed) Problems
Prof. S. Lakshmivarahan · IIT Madras
Credits and licence
- From
- Dynamic Data Assimilation: an introduction, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- An Overview
- Data Mining, Data assimilation and prediction
- A classification of forecast errors
- Finite Dimensional Vector Space
- Matrices
- Matrices Continued
- Multi-variate Calculus
- Optimization in Finite Dimensional Vector spaces
- Deterministic, Static, linear Inverse (well-posed) Problems
- Deterministic, Static, Linear Inverse (Ill-posed) Problems
- A Geometric View – Projections
- Deterministic, Static, nonlinear Inverse Problems
- On-line Least Squares
- Examples of static inverse problems
- Interlude and a Way Forward
- Matrix Decomposition Algorithms
- Matrix Decomposition Algorithms Continued
- Minimization algorithms
- Minimization algorithms Continued
- Inverse problems in deterministic
- Inverse problems in deterministic Continued
- Forward sensitivity method
- Relation between FSM and 4DVAR
- Statistical Estimation
- Statistical Least Squares
- Maximum Likelihood Method
- Bayesian Estimation
- From Gauss to Kalman-Linear Minimum Variance Estimation
- Initialization Classical Method
- Optimal interpolations
- A Bayesian Formation-3D-VAR methods
- Linear Stochastic Dynamics - Kalman Filter
- Linear Stochastic Dynamics - Kalman Filter Continued
- Linear Stochastic Dynamics - Kalman Filter Continued.
- Covariance Square Root Filter
- Nonlinear Filtering
- Ensemble Reduced Rank Filter
- Basic nudging methods
- Deterministic predictability
- Predictability A stochastic view and Summary