EML 6229 Introduction to Random Dynamical Systems Mrinal Kumar Assistant Prof., MAE

Slides:



Advertisements
Similar presentations
Uncertainty Quantification (UQ) and Climate Change Talking Points Mark Berliner, Ohio State Issues of continuing interest: Models, Data, Impacts & Decision.
Advertisements

Slide 1 ILLINOIS - RAILROAD ENGINEERING Railroad Hazardous Materials Transportation Risk Analysis Under Uncertainty Xiang Liu, M. Rapik Saat and Christopher.
CHAPTER 40 Probability.
Markov Processes Aim Higher. What Are They Used For? Markov Processes are used to make predictions and decisions where results are partly random but may.
IEOR 4004: Introduction to Operations Research Deterministic Models January 22, 2014.
Design of Experiments Lecture I
Ordinal Optimization. Copyright by Yu-Chi Ho2 Some Additional Issues F Design dependent estimation noises vs. observation noise F Goal Softening to increase.
Page 1© Crown copyright 2006ESWWIII, Royal Library of Belgium, Brussels, Nov 15 th 2006 Forecasting uncertainty: the ensemble solution Mike Keil, Ken Mylne,
Uncertainty in Engineering The presence of uncertainty in engineering is unavoidable. Incomplete or insufficient data Design must rely on predictions or.
Evaluating Inforce Blocks Of Disability Business With Predictive Modeling SOA Spring Health Meeting May 28, 2008 Jonathan Polon FSA
Probability & Counting Rules Chapter 4 Created by Laura Ralston Revised by Brent Griffin.
Monte Carlo Localization for Mobile Robots Karan M. Gupta 03/10/2004
Validating uncertain predictions Tony O’Hagan, Leo Bastos, Jeremy Oakley, University of Sheffield.
2012: Hurricane Sandy 125 dead, 60+ billion dollars damage.
Planning under Uncertainty
Earthquake Probabilities in the San Francisco Bay Region, 2002–2031 Working Group on California Earthquake Probabilities, 2002 Chapters 1 & 2.
Thinking About Catastrophic Events Lesson 1. Targets Lesson Internal and external processes of earth systems cause natural hazards. 1.2 Natural.
Marakas: Decision Support Systems, 2nd Edition © 2003, Prentice-Hall Chapter Chapter 4: Modeling Decision Processes Decision Support Systems in the.
B1 -Biogeochemical ANL - Townhall V. Rao Kotamarthi.
Monitoring and Pollutant Load Estimation. Load = the mass or weight of pollutant that passes a cross-section of the river in a specific amount of time.
© 2003 by Davi GeigerComputer Vision November 2003 L1.1 Tracking We are given a contour   with coordinates   ={x 1, x 2, …, x N } at the initial frame.
1 11 Lecture 12 Overview of Probability and Random Variables (I) Fall 2008 NCTU EE Tzu-Hsien Sang.
MA-250 Probability and Statistics Nazar Khan PUCIT Lecture 10.
What Is the Probability? The chance that something will happen is called probability. For example, if you toss a penny, what is the probability that it.
INFERENTIAL STATISTICS – Samples are only estimates of the population – Sample statistics will be slightly off from the true values of its population’s.
Data-assimilation in flood forecasting for the river Rhine between Andernach and Düsseldorf COR-JAN VERMEULEN.
Gaussian process modelling
Discussion on Modeling Stefan Finsterle Earth Sciences Division Lawrence Berkeley National Laboratory 29. Task Force Meeting Lund, Sweden November 29-29,
Equation-Free (EF) Uncertainty Quantification (UQ): Techniques and Applications Ioannis Kevrekidis and Yu Zou Princeton University September 2005.
Accuracy and Precision
Chapter 2: The Scientific Method and Environmental Sciences.
Weather Instruments. The weather forecast that helped you plan activities for this week was probably made by a meteorologist. A meteorologist is a person.
Probabilistic Robotics Bayes Filter Implementations Gaussian filters.
Modelling and Simulations The Kingsway School. What are Computer Models? When a real life situation is represented by computer software. Can you think.
Statistics and the Verification Validation & Testing of Adaptive Systems Roman D. Fresnedo M&CT, Phantom Works The Boeing Company.
PH4705 & ET4305: Measurements Measurement: assign numbers to property of object or event to describe it The absolute true value of a measurement can’t.
ECE 466/658: Performance Evaluation and Simulation Introduction Instructor: Christos Panayiotou.
2 Introduction to Kalman Filters Michael Williams 5 June 2003.
NCAF Manchester July 2000 Graham Hesketh Information Engineering Group Rolls-Royce Strategic Research Centre.
Cosmological Model Selection David Parkinson (with Andrew Liddle & Pia Mukherjee)
Inference: Probabilities and Distributions Feb , 2012.
10th Annual George Mason University Conference on Atmospheric Transport and Dispersion Modeling Debbie Payton and Mark Miller Hazardous Materials Response.
Systems Realization Laboratory The Role and Limitations of Modeling and Simulation in Systems Design Jason Aughenbaugh & Chris Paredis The Systems Realization.
Decision Making Under Uncertainty - Bayesian Techniques.
November 3 IDR To do: What we have, what we’d like to do Modeling Mathematical/numerical approaches What we need inside the codes Tasks.
Tracking with dynamics
- 1 - Computer model under uncertainty In previous lecture on accuracy assessment –We considered mostly deterministic models. – We did not distinguish.
Introduction to Sampling Methods Qi Zhao Oct.27,2004.
Quantifying Uncertainty Associated with Hurricane Path Julie LETSCHERT, INSA LYON (Mentor: Svetlana POROSEVA, Florida State University)
Futron Corporation 2021 Cunningham Drive, Suite 303 Hampton, Virginia Phone Fax Results You Can Trust Assessing.
Planning Under Uncertainty. Sensing error Partial observability Unpredictable dynamics Other agents.
Don Resio, Senior Scientist, US Army ERDC-CHL Data Needs for Coastal Storm Surge Estimation IbTrACS Meeting Honolulu, HI April 12, 2011.
Introduction to Probability. What is probability? A number between 0 and 1 (inclusive) that gives us an idea of how likely it is that an event will occur.
TRB brief 1 FAA TFMM Program TFM Research Board (TRB) Meeting Northrup Grumman 16 October 2008.
Probabilistic Robotics Bayes Filter Implementations Gaussian filters.
Uncertain Judgements: Eliciting experts’ probabilities Anthony O’Hagan et al 2006 Review by Samu Mäntyniemi.
REC 2008; Zissimos P. Mourelatos Design under Uncertainty using Evidence Theory and a Bayesian Approach Jun Zhou Zissimos P. Mourelatos Mechanical Engineering.
Using uncertainty to test model complexity Barry Croke.
Dealing with Uncertainty: A Survey of Theories and Practice Yiping Li, Jianwen Chen and Ling Feng IEEE Transactions on Knowledge and Data Engineering,
Uncertainty quantification in generic Monte Carlo Simulation: a mathematical framework How to do it? Abstract: Uncertainty Quantification (UQ) is the capability.
holds a Ph. D. in tropical meteorology, M. Tech
COmbining Probable TRAjectories — COPTRA
HOW SMALL PROBABILITIES AFFECT OUR LIFE?
Meteorological Instrumentation and Observations
Uncertain, High-Dimensional Dynamical Systems
Additional notes on random variables
Measurement What is it and why do it? 2/23/2019
Additional notes on random variables
CS 188: Artificial Intelligence Fall 2008
Real-time Uncertainty Output for MBES Systems
Presentation transcript:

EML 6229 Introduction to Random Dynamical Systems Mrinal Kumar Assistant Prof., MAE

Syllabus…

Uncertainty: A Fundamental Challenge ✣ Nature is far too complex for engineers ✣ Understanding nature: via math:: analysis via observation:: instruments Both imperfect!! ✣ Outcome: we must live with uncertainty, aka stochasticity Specifically, we must make decisions while limited by stochastic information!

Uncertainty: Examples Weather prediction Hazardous event management Catastrophic event decision making Apophis collision probability in 2029: 2.7% (2004 estimate) Understanding turbulence: affordable transportation 2010 Iceland eruption map

due to practical limitations, e.g.  model not good enough,  not enough measurements available,  neglected effects Characterization of Uncertainty ✣ Given that uncertainty is unavoidable, how do we best capture it in engineering/scientific/financial/economic/sociological systems? Uncertainty Quantification (UQ) Uncertainty Types Epistemic: Aleatory: due to fundamental limitations, e.g.  accuracy of instruments  computational limits  nonrepeatability of experiments  Epistemic uncertainty is reducible to aleatory uncertainty in an ideal world

Uncertainty in System Models System (physics)…. to be modeled States … entities that identify/characterize/quant ify the system Accurate math model (no uncertainty) …but too complex!!

Uncertainty in System Models A much simpler, reduced order model, but with uncertainty noise…. Also need: initial conditions: almost always come from measurements

Uncertainty in Measurements ✣ There is always aleatory measurement uncertainty: unavoidable and irreducible ✣ In today’s world, epistemic measurement uncertainty is also dominant (essentially too much information to track, and too few resources) Example: consider the so-called potentially hazardous asteroids

Uncertainty in Measurements ✣ There is always aleatory measurement uncertainty: unavoidable and irreducible ✣ In today’s world, epistemic measurement uncertainty is also dominant (essentially too much information to track, and too few resources) Example: or something closer to home: our space debris View of debris in LEO Expanded view of debris to include HEO plus…. active satellites!! limited resources….

Uncertainty Propagation ✣ When measurements cannot be made (due to lack of allocation), only way to quantify uncertainty is to propagate (forecast) it through use the best known models