Math 2.0

Resources / course

Dynamic Data Assimilation: an introduction 4Q4GSJAK8D

URIm2://res/4Q4GSJAK8D
Kindcourse
CreatorsS. Lakshmivarahan (instructor)
Date2016-02-08
Scopein
Idscourse 03v0r5n49:111106082

Expressions

e1: edition

Languageen
Date2016-02-08
Contents
  • part 1: Introduction
  • session 1: An Overview
  • session 2: Data Mining, Data assimilation and prediction
  • session 3: A classification of forecast errors
  • part 2: Mathematical Tools
  • session 4: Finite Dimensional Vector Space
  • session 5: Matrices
  • session 6: Matrices Continued
  • session 7: Multi-variate Calculus
  • session 8: Optimization in Finite Dimensional Vector spaces
  • part 3: Deterministic, Static Inverse Problems
  • session 9: Deterministic, Static, linear Inverse (well-posed) Problems
  • session 10: Deterministic, Static, Linear Inverse (Ill-posed) Problems
  • session 11: A Geometric View – Projections
  • session 12: Deterministic, Static, nonlinear Inverse Problems
  • session 13: On-line Least Squares
  • session 14: Examples of static inverse problems
  • part 4: Numerical Algorithms for solving Least Squares Problems
  • session 15: Interlude and a Way Forward
  • session 16: Matrix Decomposition Algorithms
  • session 17: Matrix Decomposition Algorithms Continued
  • session 18: Minimization algorithms
  • session 19: Minimization algorithms Continued
  • part 5: Deterministic, Dynamic Inverse Problems
  • session 20: Inverse problems in deterministic
  • session 21: Inverse problems in deterministic Continued
  • session 22: Forward sensitivity method
  • session 23: Relation between FSM and 4DVAR
  • part 6: Principles of Statistical Estimation
  • session 24: Statistical Estimation
  • session 25: Statistical Least Squares
  • session 26: Maximum Likelihood Method
  • session 27: Bayesian Estimation
  • session 28: From Gauss to Kalman-Linear Minimum Variance Estimation
  • part 7: Stochastic, Static Inverse Problems
  • session 29: Initialization Classical Method
  • session 30: Optimal interpolations
  • session 31: A Bayesian Formation-3D-VAR methods
  • part 8: Stochastic, Dynamic Inverse Problems
  • session 32: Linear Stochastic Dynamics - Kalman Filter
  • session 33: Linear Stochastic Dynamics - Kalman Filter Continued
  • session 34: Linear Stochastic Dynamics - Kalman Filter Continued.
  • session 35: Covariance Square Root Filter
  • session 36: Nonlinear Filtering
  • session 37: Ensemble Reduced Rank Filter
  • part 9: Nudging Methods
  • session 38: Basic nudging methods
  • part 10: Predictability
  • session 39: Deterministic predictability
  • session 40: Predictability A stochastic view and Summary

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