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Concepts index

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Index Entry  Section

$
$dimetaphi: 2.3.1 Setup a model and compile with cmz
$zinit: 2.3.1 Setup a model and compile with cmz

A
adjoint: 4.2.1 Overview of optimisation with Miniker
aspha.data: 2.3.3 Running a simulation and using the output
aspha.data’, GTLS: 4.6.3 Generalized tangent linear system analysis run and output
aspha.data’, SVD: 4.5.3 Singular Value Decomposition run and output

B
Borel sweep: 4.4 Feedback gain
Borel sweep graphics: 4.4.2 Borel sweep results
Borel sweep results: 4.4.2 Borel sweep results

C
cells: Introduction
cernlib: A.2 Common requisites
command law: 4.2.2 Control laws
compilation: 2.3.2 Setup a model and compile with make
configuration of source: A.4.2 Configuration
controlling the run: 2.4 Controlling the run

D
data.data: 3.8.2 Data
data.data: 4.3.2.2 Kalman filter results
dimetaphi: 3.9.1 The explicit size sequence
dimetaphi: 3.9.1 The explicit size sequence
dimetaphi’, Kalman filter: 4.3.1.1 Kalman filter vectors dimensions
down node: 3.3 Describing 1D gridded model
dres.data: 2.3.3 Running a simulation and using the output
dres.data: 2.3.3 Running a simulation and using the output
dres.data’, GTLS: 4.6.3 Generalized tangent linear system analysis run and output

E
equations, grid: Grid node equations
error vector dimension: 4.3.1.1 Kalman filter vectors dimensions

F
FDL, GNU Free Documentation License: C.1 GNU Free Documentation License
feature setting: 3.1 Overview of additional features setting
Feedback gain: 4.4 Feedback gain
ffl (linearity test): 2.4.1 Executing code at the end of each time step
final cost: 4.2.1 Overview of optimisation with Miniker

G
Generalized linear tangent system: 4.6 Generalized linear tangent system analysis
gradpj.data: 4.2.4 Sensitivity of cost function to parameters
graphics: 2.3.4 Doing graphics
graphics with gnuplot: 2.3.4 Doing graphics
graphics with PAW: 2.3.4 Doing graphics
graphics, Borel sweep: 4.4.2 Borel sweep results
GTLS: 4.6 Generalized linear tangent system analysis
GTLS output: 4.6.3 Generalized tangent linear system analysis run and output
GTLS run: 4.6.3 Generalized tangent linear system analysis run and output

H
Heaviside function: 3.6 Rule of programming non continuous models

I
initial variance-covariance on states: Initial variance-covariance matrix on the state
installation with make: A.4.3 Installation with make
integrand cost: 4.2.1 Overview of optimisation with Miniker

K
Kalman filter: 4.3 Kalman filter
Kalman filter output: 4.3.2.2 Kalman filter results
Kalman filter results: 4.3.2.2 Kalman filter results

L
lapack: A.2 Common requisites
limiting conditions: Limiting conditions
linearity test: 2.4.1 Executing code at the end of each time step
logical flags: 3.1 Overview of additional features setting
Lyapunov exponents: 4.6 Generalized linear tangent system analysis

M
Makefile.miniker: 5.1 Make variables
Makefile.sltc: 4.5.2 Singular Value Decomposition with make
Makefile.sltcirc: 4.6.2 Generalized tangent linear system with make
mini_ker.cmz: A.3 Miniker with cmz
mod: 2.3.1 Setup a model and compile with cmz
model equations: 3.9.2 Entering the model equations, with explicit sizes
model size: 3.9.1 The explicit size sequence
Model.hlp: 2.3.3 Running a simulation and using the output
mortran: Introduction
mortran: 2.2.1 All you need to know about mortran and cmz directives
mortran, with make: A.4.1 Additional requirements for Miniker with make

O
obs.data: 3.8.1 Observations
observation function: 3.8.1 Observations
observations: 4.3.1.2 Error and observation matrices
observations, general: 4.3 Kalman filter
optimisation: 4.2.1 Overview of optimisation with Miniker
output file: 2.3.3 Running a simulation and using the output
output, GTLS: 4.6.3 Generalized tangent linear system analysis run and output
output, Kalman filter: 4.3.2.2 Kalman filter results
output, sensitivity: 4.1 Automatic sensitivity computation
output, SVD: 4.5.3 Singular Value Decomposition run and output

P
printing: 2.4.2 Controlling the printout and data output
Programming environments: A.1 Programming environments
propagator: 4.6 Generalized linear tangent system analysis

R
requirements, with make: A.4.1 Additional requirements for Miniker with make
res.data: 2.3.3 Running a simulation and using the output
results, Borel sweep: 4.4.2 Borel sweep results
results, Kalman filter: 4.3.2.2 Kalman filter results
run, GTLS: 4.6.3 Generalized tangent linear system analysis run and output
run, SVD: 4.5.3 Singular Value Decomposition run and output
running model: 2.3.3 Running a simulation and using the output

S
select flag: 3.1 Overview of additional features setting
selseq.kumac: 3.1 Overview of additional features setting
selseq.kumac: A.3 Miniker with cmz
sens.data: 4.1 Automatic sensitivity computation
sensitivities: 4.1 Automatic sensitivity computation
sensitivity, output: 4.1 Automatic sensitivity computation
sequence: 2.1 General structure of the code
sequences: 2 Miniker model programming
sigma.data: 4.1 Automatic sensitivity computation
Singular Value Decomposition: 4.5 Stability analysis of fastest modes
sltc.exe: 4.5 Stability analysis of fastest modes
sltc.exe: 4.5.3 Singular Value Decomposition run and output
sltcirc.exe: 4.6 Generalized linear tangent system analysis
sltcirc.exe: 4.6.3 Generalized tangent linear system analysis run and output
smod: 4.5.1 Singular Value Decomposition with cmz
smod: 4.6.1 Generalized tangent linear system with cmz
starting point: Starting points
state matrix: 4.5 Stability analysis of fastest modes
SVD: 4.5 Stability analysis of fastest modes
SVD output: 4.5.3 Singular Value Decomposition run and output
SVD run: 4.5.3 Singular Value Decomposition run and output

T
TEF: Introduction
TEF: 2.2 Miniker programming illustrated
title file: title file
title.tex: title file
title.tex’, GTLS: 4.6.3 Generalized tangent linear system analysis run and output
title.tex’, SVD: 4.5.3 Singular Value Decomposition run and output
tr.data: 2.3.3 Running a simulation and using the output
transfers: Introduction

U
up node: 3.3 Describing 1D gridded model
uxcom.data: 4.2.2 Control laws
uycom.data: 4.2.2 Control laws

V
variance-covariance error: Error variance-covariance matrices
variance-covariance matrices: 4.3.1.2 Error and observation matrices
variance-covariance matrices, general: 4.3 Kalman filter
variance-covariance matrix on state: Observations and error on state matrix

Z
zcmd_law: 4.2.2 Control laws
zcmd_law.inc: 4.2.2 Control laws
zinit: 2.2.2 Entering model equation and parameters
zinit, general: 2.1 General structure of the code
zinit’, Kalman filter: 4.3.1.2 Error and observation matrices
zinit.mti: 2.3.2 Setup a model and compile with make
ZOOM: Introduction
zsteer: 2.4.1 Executing code at the end of each time step
zsteer’, Kalman filter: 4.3.2.1 Feeding the observations to the model
zsteer.inc: 2.4.1 Executing code at the end of each time step

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