Download Error Budget - Roger Ghanem

Workshop on
the Elements of Predictability
Roger Ghanem
John Red-Horse
Steve Wojtkiewicz
Thanks to:
Department of Civil Engineering at Hopkins
National Science Foundation
LOGISTICS
BACKGROUND AND INTRODUCTION
Logistics
• Today:
•Lunch provided in this room
•Sign-up for two of tomorrow’s breakout sessions
•morning / afternoon
•Banquet in Shriver Hall: 5pm-7:30pm
•Restrooms
• Tomorrow:
•Lunch and coffee breaks provided in Shriver Hall
•Meeting rooms in Mattin Hall (three sessions) and Shriver Hall
•Program has been moved ahead by 30 minutes
Background and Introduction
This is the Second Hopkins Workshop in Uncertainty-Related Research
1999 Workshop on Uncertainty Analysis and Management
identified, qualitatively, components of an emerging field
2003 Elements of Predictability
aim for a more quantitative and specific delineation of these
components
Both meetings recognize and capitalize on the multidisciplinary interests,
contributions, and intrinsic nature of the relevant questions.
Two sides to the problem:
uncertainty quantification and management
WHAT LEVEL OF ACCURACY IS CONSISTENT WITH AVAILABLE
RESOURCES:
DATA
ANALYSIS
COMPUTING
WHAT LEVEL OF RESOURCES:
DATA
ANALYSIS
COMPUTING
IS REQUIRED TO ACHIEVE TARGET ACCURACY / CONFIDENCE
PHYSICAL
REALITY:
SURROGATE
TO REALITY:
• ASSUMED PHYSICS
• EXPERIMENTAL DATA
REPRESENT TRUNCATED
INFORMATION:
PROBABILISTIC MODELS,
SUBSCALE, MULTISCALE
MODELS.
ASSIMILATE
PREDICTIVE MODEL
Approximation
to surrogate
OBJECTIVE
Decision
TYPICAL QUESTION ENABLED BY
UNCERTAINTY QUANTIFICATION AND MANAGEMENT
AT WHAT LEVEL OF CONFIDENCE CAN ONE STREAM OF
INFORMATION BE SUBSTITUTED FOR ANOTHER ONE:
SMALL-SCALE TESTING FOR FULL-SCALE TESTING
MODEL-BASED PREDICTIONS FOR TESTING
FUSION OF HISTORICAL DATA, MODELING AND
TESTING
Components of a predictive model
Packaging of information
1.
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Packaging of data:
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Probabilistic models
Fuzzy, convex, etc… models
Packaging of knowledge:
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Mechanistic models
Expertise
2.
Experimental data
3.
Quantity of interest
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Life expectancy
Probability of mission success
Maximum stress at weld for mission profile
Probability of failure
etc
Difference between predicted and actual performance
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Error depends both on models of physical
behavior as well as the tools and methods to
acquire the necessary data for these models.
Error budget
Interaction of model and data:
Unified mathematical framework:
Management of uncertainty
COORDINATES IN THIS SPACE REPRESENT PROBABILISTIC CONTENT.
SENSITIVITY OF PROBABILISTIC STATEMENTS OF BEHAVIOR ON DATA.
Multi-scale probabilistic model
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Index the stochastic process by SCALE.
Only measured scales make it into the stochastic model
of the parameter: Approximation space is the completion
of the hull generated by the measurements.
Variability in each measured scale represents the
magnitude of the contribution from that scale.
We need a set of spatial functions to serve as carriers of
fluctuation: WAVELETS are well-adapted to
scale-localization.
Complex interacting systems:
Multi-physics – Multi-scales
System complexity is a significant source of uncertainty eg.
• unmodeled dynamics
• interaction between fluctuations
New tools of probabilistic modeling are being developed for proper
representation of complexity:
• uncertainty joints
• nonparametric models
• multi-scale models
Applications completed/current in:
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Collaborations:
Structural Dynamics
Dynamic Soil-Structure Interaction
JHU
Structural Acoustics
Sandia
Acoustic Scattering
WPAFB
Compressible and Incompressible Flows
Micro-Fluidics
Universite Marne-la-Vallee
Reactive Flows: Combustion
Universite d’Evry
Reactive Flows: Protein Labeling
University of Oklahoma
Fracture/Fatigue of Composites
Flow and Transport in Porous Media
Taisei Corporation
Rock Mechanics
Hydrology and Watershed Management
UNDEX
Aerodynamics/Aeroelasticity
Drilling