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Uncertain, High-Dimensional Dynamical Systems. Igor Mezić. University of California, Santa Barbara. IPAM, UCLA, February 2005. Introduction. Measure of uncertainty? Uncertainty and spectral theory of dynamical systems. Model validation and data assimilation. Decompositions. - PowerPoint PPT Presentation
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Uncertain, High-Dimensional Uncertain, High-Dimensional Dynamical SystemsDynamical Systems
Igor MezićIgor Mezić
University of California, Santa BarbaraUniversity of California, Santa Barbara
IPAM, UCLA, February 2005IPAM, UCLA, February 2005
IntroductionIntroduction
•Measure of uncertainty? •Uncertainty and spectral theory of dynamical systems.•Model validation and data assimilation.•Decompositions.
x
Dynamical evolution of uncertainty: an exampleDynamical evolution of uncertainty: an example
Input measureInput measure
Output measureOutput measure
Tradeoff:Tradeoff: Bifurcation Bifurcation vs.vs. contracting dynamicscontracting dynamics
Dynamical evolution of uncertainty: general set-upDynamical evolution of uncertainty: general set-up
Skew-product system.Skew-product system.
Dynamical evolution of uncertainty: general set-upDynamical evolution of uncertainty: general set-up
Dynamical evolution of uncertainty: general set-upDynamical evolution of uncertainty: general set-up
ff ff
TT
F(z)F(z)11
00
Dynamical evolution of uncertainty: simple examplesDynamical evolution of uncertainty: simple examples
Expanding maps: Expanding maps: x’=2xx’=2x
FF(z)(z)11
00
A measure of uncertainty of an observableA measure of uncertainty of an observable
11
00
Computation of uncertainty in CDF metricComputation of uncertainty in CDF metric
Maximal uncertainty for CDF metricMaximal uncertainty for CDF metric
11
00
Variance, Entropy and Uncertainty in CDF metricVariance, Entropy and Uncertainty in CDF metric
00
Uncertainty in CDF metric: Pitchfork bifurcationUncertainty in CDF metric: Pitchfork bifurcation
x
Input measureInput measure
Output measureOutput measure
Time-averaged uncertaintyTime-averaged uncertainty
11
00
ConclusionsConclusions
IntroductionIntroduction
• Example: thermodynamics from statistical mechanics
PV=NkTPV=NkT
“…“…Any rarified gas will behave that way, Any rarified gas will behave that way, no matter how queer the dynamics of its particles…”no matter how queer the dynamics of its particles…”
Goodstein (1985)Goodstein (1985)
•Example: Galerkin truncation of evolution equations.
•Information comes from a single observable time-series.
IntroductionIntroduction
When do two dynamical systemsWhen do two dynamical systems exhibit exhibit similar behaviorsimilar behavior??
IntroductionIntroduction
• Constructive proof that Constructive proof that ergodic partitionsergodic partitions and and invariant invariant measuresmeasures of systems can be compared using a of systems can be compared using a single observablesingle observable (“ (“Statistical Takens-AeyelsStatistical Takens-Aeyels” theorem).” theorem).• A A formalismformalism based on based on harmonic analysisharmonic analysis that extends that extends the concept of comparing the invariant measure.the concept of comparing the invariant measure.
Set-upSet-up
Time averages and invariant measures:
Set-upSet-up
Pseudometrics for Dynamical SystemsPseudometrics for Dynamical Systems
wherewhere
• PseudometricPseudometric that captures statisticsthat captures statistics::
Ergodic partitionErgodic partition
Ergodic partitionErgodic partition
An example: analysis of experimental dataAn example: analysis of experimental data
Go( j )
Gc( j)
+
-P ressureD isturbance
V alvecommand
P lant: 2 n d orderw it h delay
P hase-shi ftingcon trol ler
V alv e saturation
Analysis of experimental dataAnalysis of experimental data
Analysis of experimental dataAnalysis of experimental data
Koopman operator, triple decomposition, Koopman operator, triple decomposition, MODMOD
-Efficient representation of the flow field; can be done with vectors -Lagrangian analysis:
MEANMEAN FLOW FLOW FLUCTUATIONSFLUCTUATIONS
Desirable: “Triple decomposition”: PERIODICPERIODIC APERIODICAPERIODIC
EmbeddingEmbedding
Conclusions
• Constructive proof that Constructive proof that ergodic partitionsergodic partitions and and invariant invariant measuresmeasures of systems can be compared using a of systems can be compared using a single observable –deterministic+stochastic.single observable –deterministic+stochastic.• A A formalismformalism based on based on harmonic analysisharmonic analysis that extends that extends the concept of comparing the invariant measurethe concept of comparing the invariant measure• PseudometricsPseudometrics on spaces of dynamical systems. on spaces of dynamical systems.• Statistics – basedStatistics – based, , linearlinear (but (but infinite-dimensionalinfinite-dimensional).).
Everson et al., JPO 27 (1997)Everson et al., JPO 27 (1997)
IntroductionIntroduction
Everson et al., JPO 27 (1997)Everson et al., JPO 27 (1997)
IntroductionIntroduction
-4 modes -99% 4 modes -99% of the of the variance!variance!-no -no dynamicsdynamics!!
IntroductionIntroduction
In this talk: In this talk:
-Account explicitly forAccount explicitly for dynamics dynamics to produce a decomposition.to produce a decomposition.-Tool: -Tool: lift lift to infinite-dimensional space of functions on attractor to infinite-dimensional space of functions on attractor + + consider properties of consider properties of Koopman operator.Koopman operator.-Allows for detailed comparison of -Allows for detailed comparison of dynamical propertiesdynamical properties of of the evolution and retained modes.the evolution and retained modes.-Split into -Split into “deterministic”“deterministic” and and “stochastic”“stochastic” parts: useful for parts: useful for prediction purposes.prediction purposes.
Factors and harmonic analysisFactors and harmonic analysis
Example:
Factors and harmonic analysisFactors and harmonic analysis
Harmonic analysis: an exampleHarmonic analysis: an example
x x a x
y y x a x
'
'
sin( ),
sin( ).
= + +
= + +
π
π
2
2
Evolution equations and Koopman Evolution equations and Koopman operatoroperator
Evolution equations and Koopman Evolution equations and Koopman operatoroperator
Similar:“Wold decomposition”Similar:“Wold decomposition”
Evolution equations and Koopman Evolution equations and Koopman operatoroperator
But how to get this from data?But how to get this from data?
Evolution equations and Koopman Evolution equations and Koopman operatoroperator
-Remainder has continuous spectrum!
is almost-periodic.
ConclusionsConclusions
-Use properties of the Use properties of the Koopman operator Koopman operator to produce a decompositionto produce a decomposition-Tool: -Tool: lift lift to infinite-dimensional space of functions on attractor.to infinite-dimensional space of functions on attractor.-Allows for detailed comparison of -Allows for detailed comparison of dynamical propertiesdynamical properties of of the evolution and retained modes.the evolution and retained modes.-Split into -Split into “deterministic”“deterministic” and and “stochastic”“stochastic” parts: useful for parts: useful for prediction purposes.prediction purposes.
-Useful for Lagrangian studies in fluid mechanics.-Useful for Lagrangian studies in fluid mechanics.
Invariant measures and time-averagesInvariant measures and time-averages
Example:Example: Probability histogramsProbability histograms!!
-1 0
κ i
2• But But poor for dynamicspoor for dynamics: :
IrrationalIrrational ’s’sproduce the same statisticsproduce the same statistics
Dynamical evolution of uncertainty: simple examplesDynamical evolution of uncertainty: simple examples
Types of uncertainty:Types of uncertainty:
•EpistemicEpistemic (reducible) (reducible)•Aleatory Aleatory (irreducible)(irreducible)•A-prioriA-priori (initial conditions, (initial conditions, parameters, model structure)parameters, model structure)•A-posterioriA-posteriori (chaotic dynamics, (chaotic dynamics, observation error)observation error)
Expanding maps: Expanding maps: x’=2xx’=2x
Uncertainty in CDF metric: ExamplesUncertainty in CDF metric: Examples
Uncertainty strongly dependent on distribution of initial conditions.Uncertainty strongly dependent on distribution of initial conditions.