efficient, accurate, and non-gaussian statistical error propagation through nonlinear system models...
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Efficient, Accurate, and Non-Gaussian Statistical Error Propagation Through
Nonlinear System Models
Travis V. Anderson July 26, 2011
Graduate Committee: Christopher A. Mattson
David T. Fullwood
Kenneth W. Chase
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Presentation Outline
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Section 1: Introduction & Motivation
Section 2: Uncertainty Analysis Methods
Section 3: Propagation of Variance
Section 4: Propagation of Skewness & Kurtosis
Section 5: Conclusion & Future Work
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Section 1:Introduction & Motivation
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Engineering Disasters
Tacoma Narrows Bridge
Hindenburg
Space Shuttle Challenger
Chernobyl
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F-35 Joint-Strike Fighter
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Research Motivation
• Allow the system designer to quantify system model accuracy more quickly and accurately
• Allow the system designer to verify design decisions at the time they are made
• Prevent unnecessary design iterations and system failures by creating better system designs
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Section 2:Uncertainty Analysis Methods
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Uncertainty Analysis Methods
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• Error Propagation via Taylor Series Expansion• Brute Force Non-Deterministic Analysis
(Monte Carlo, Latin Hypercube, etc.)• Deterministic Model Composition• Error Budgets• Univariate Dimension Reduction• Interval Analysis• Bayesian Inference• Response Surface Methodologies• Anti-Optimizations
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Brute Force Non-Deterministic Analysis
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• Fully-described, non-Gaussian output distribution can be obtained
• Simulation must be executed again each time any input changes
• Computationally expensive
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Deterministic Model Composition
• A compositional system model is created• Each component’s error is included in an error-
augmented system model• Component error values are varied as the model is
executed repeatedly to determine max/min error bounds
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Error Budgets
• Error in one component is perturbed at a time• Each perturbation’s effect on model output is
observed• Either errors must be independent or a separate
model of error interactions is required
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Univariate Dimension Reduction
• Data is transformed from a high-dimensional space to a lower-dimensional space
• In some situations, analysis in reduced space may be more accurate than in the original space
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Interval Analysis
• Measurement and rounding errors are bounded• Arithmetic can be performed using intervals instead of a
single nominal value• Many software languages, libraries, compilers, data types,
and extensions support interval arithmetic • XSC, Profil/BIAS, Boost, Gaol, Frink, MATLAB (Intlab)
• IEEE Interval Standard (P1788)
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Bayesian Inference
• Combines common-sense knowledge with observational evidence
• Meaningful relationships are declared, all others are ignored
• Attempts to eliminate needless model complexity
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Response Surface Methodologies
• Typically uses experimental data and design of experiments techniques
• An n-dimensional response surface shows the output relationship between n-input variables
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Anti-Optimizations
• Two-tiered optimization problem• Uncertainty is anti-optimized on a lower level to
find the worst-case scenario• The overall design is then optimized on a higher-
level to find the best design
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Section 3:Propagation of Variance
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Central Moments
• 0th Central Moment is 1• 1st Central Moment is 0• 2nd Central Moment is variance• 3rd Central Moment is used to calculate skewness• 4th Central Moment is used to calculate kurtosis
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First Order Taylor Series
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First-Order Formula Derivation
Square and take the Expectation of both sides:
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Assumption:• Inputs are independent
CovarianceTerm
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First-Order Error Propagation
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• Formula for error propagation most-often cited in literature
• Frequently used “blindly” without an appreciation of its underlying assumptions and limitations
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Assumptions and Limitations
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1. The approximation is generally more accurate for linear models This Section
2. Only variance is propagated and higher-order statistics are neglected Section 4
3. All inputs are assumed be Gaussian Section 4
4. System outputs and output derivatives can be obtained
5. Taking the Taylor series expansion about a single point causes the approximation to be of local validity only
6. The input means and standard deviations must be known
7. All inputs are assumed to be independent
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First-Order Accuracy
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Function: y = 1000sin(x)Input Variance: 0.2
100% ErrorUnacceptable!
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Second-Order Error Propagation
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Just as before:
1. Subtract the expectation of a second-order Taylor series from a second-order Taylor series
2. Square both sides, and take the expectation
Odd moments are zero
Assumption:• Inputs are Gaussian
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Second-Order Error Propagation
• Second-order formula for error propagation most-often cited in literature
• Like the first-order approximation, the second-order approximation is also frequently used “blindly” without an appreciation of its underlying assumptions and limitations
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Second-Order Accuracy
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Function: y = 1000sin(x)Input Variance: 0.2
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Higher-Order Accuracy
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Function: y = 1000sin(x)Input Variance: 0.2
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Computational Cost
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Predicting Truncation Error
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• How can we achieve higher-order accuracy with lower-order cost?
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Predicting Truncation Error
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• Can Truncation Error Be Predicted?
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Adding A Correction Factor
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Trigonometric (2nd Order): y = sin(x) or y = cos(x)
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Trigonometric Correction Factor
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Correction Factors
Exponential (1st Order): y = exp(x)
Natural Log (1st Order): y = ln(x)
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Correction Factors
where:
Exponential (1st Order): y = bx
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So What Does All This Mean?
• We can achieve higher-order accuracy with lower-order computational cost
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Computational CostAverage Error
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Kinematic Motion of Flapping Wing
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Accuracy of Variance Propagation
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Order2nd:3rd:4th:CF:
RMS Rel. Err.40.97%11.18%1.32%1.96%
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Computational Cost
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Execution time was reduced from ~70 minutes to ~4 minutes
A computational cost reduction by 1750%
Fourth-order accuracy was obtained with only second-order computational cost
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Section 4:Propagation of Skewness &
Kurtosis
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Non-Gaussian Error Propagation
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Predicted Gaussian Output Actual System Output
Predicted Non-Gaussian Output Actual System Output
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Skewness
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• Measure of a distribution’s asymmetry• A symmetric distribution has zero
skewness
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Propagation of Skewness
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• Based on a second-order Taylor series
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Kurtosis & Excess Kurtosis
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• Measure of a distribution’s “peakedness” or thickness of its tails
Kurtosis
Excess Kurtosis
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Propagation of Kurtosis
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• Based on a second-order Taylor series
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Flat Rolling Metalworking Process
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Coefficient of Friction
Roller RadiusMaximum change in material thickness achieved in a single pass
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Input Distribution
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Gaussian Error Propagation
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• Probability Overlap: 53%
Predicted Gaussian Output Actual System Output
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Non-Gaussian Error Propagation
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• Probability Overlap: 93%
Predicted Non-Gaussian Output Actual System Output
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Benefits of Higher-Order Statistics
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Gaussian Non-Gaussian
Accuracy:
Max ΔH:(99.5% success rate)
93%
7.9 cm
53%
3.0 cm
That’s a 263% reduction
in the number of passes!
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Section 5:Conclusion & Future Work
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Conclusion
• Fourth-order accuracy in variance propagation can be achieved with only first- or second-order computational cost
• Designers do not need to assume Gaussian output. A fully-described output distribution can be obtained without significant additional cost
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Future Work
• Develop predictable correction factors for other types of nonlinear functions and models
(differential equations, state-space models, etc.)• Apply correction factors to open-form models• Can correction factors be obtained for skewness and
kurtosis propagation?
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Questions?
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Variance Example: Whirlybird
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Variance Example: Whirlybird
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Compositional Model
System Model (Pitch)
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Higher-Order Stats Example: Thrust
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Thrust Output
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Higher-Order Stats Example: Thrust
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Input Distribution
Gaussian Output Non-Gaussian Output Actual Output
Overlap: 65% Overlap: 79%
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Non-Gaussian Proof
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Propagation of Skewness
Even Gaussian Inputs Produce Skewed Outputs If 2nd Derivatives Are Non-Zero
(Nonlinear Systems)