S. Lakshmivarahan: Mathematics - Dynamic Data Assimilation (U of Oklahoma)

# Click the up-left corner for the playlist of the 40 videos 

source: nptelhrd    2016年2月8日
Dynamic Data Assimilation: an introduction by Prof S. Lakshmivarahan, School of Computer Science, University of Oklahoma. For more details on NPTEL visit http://nptel.ac.in

Lec-01 An Overview 1:00:16
Lec-02 Data Mining, Data assimilation and prediction 1:04:56
Lec-03 A classification of forecast errors 27:08
Lec-04 Finite Dimensional Vector Space 48:31
Lec-05 Matrices 1:17:30
Lec-06 Matrices Continued 45:11
Lec-07 Multi-variate Calculus 50:15
Lec-08 Optimization in Finite Dimensional Vector spaces 59:46
Lec-09 Deterministic, Static, linear Inverse (well-posed) Problems 1:03:27
Lec-10 Deterministic, Static, Linear Inverse (Ill-posed) Problems 31:21
Lec-11 A Geometric View - Projections 33:21
Lec-12 Deterministic, Static, nonlinear Inverse Problems 35:22
Lec-13 On-line Least Squares 37:20
Lec-14 Examples of static inverse problems 50:28
Lec-15 Interlude and a Way Forward 14:29
Lec-16 Matrix Decomposition Algorithms 1:03:01
Lec-17 Matrix Decomposition Algorithms Continued 50:51
Lec-18 Minimization algorithms 1:10:48
Lec-19 Minimization algorithms Continued 1:06:46
Lec-20 Inverse problems in deterministic 1:10:50
Lec-21 Inverse problems in deterministic Continued 54:25
Lec-22 Forward sensitivity method 1:02:07
Lec-23 Relation between FSM and 4DVAR 44:28
Lec-24 Statistical Estimation 1:26:41
Lec-25 Statistical Least Squares 52:29
Lec-26 Maximum Likelihood Method 28:59
Lec-27 Bayesian Estimation 1:17:34
Lec-28 From Gauss to Kalman-Linear Minimum Variance Estimation 1:09:41
Lec-29 Initialization Classical Method 1:13:59
Lec-30 Optimal interpolations 59:07
Lec-31 A Bayesian Formation-3D-VAR methods 54:01
Lec-32 Linear Stochastic Dynamics - Kalman Filter 1:04:55
Lec-33 Linear Stochastic Dynamics - Kalman Filter Continued 27:52
Lec-34 Linear Stochastic Dynamics - Kalman Filter Continued. 39:48
Lec-35 Covariance Square Root Filter 49:49
Lec-36 Nonlinear Filtering 2:30:54
Lec-37 Ensemble Reduced Rank Filter 1:37:02
Lec-38 Basic nudging methods 1:05:22
Lec-39 Deterministic predictability 1:20:29
Lec-40 Predictability A stochastic view and Summary 1:17:19