Workshop - Model Calibration and Uncertainty Analysis Using PEST

Size: px
Start display at page:

Download "Workshop - Model Calibration and Uncertainty Analysis Using PEST"

Transcription

1 About PEST PEST (Parameter ESTimation) is a general-purpose, model-independent, parameter estimation and model predictive uncertainty analysis package developed by Dr. John Doherty. PEST is the most advanced software readily available for calibration and predictive uncertainty analysis of groundwater, reservoir, surface water, and other models. Using PEST you can: apply advanced and efficient regularization techniques in calibrating your models thereby extracting maximum information content from your data on the one hand and expert knowledge on the other hand undertake linear and nonlinear predictive error and uncertainty analysis of model outputs simultaneously parameterize and calibrate several models using multiple datasets accommodate heterogeneity using advanced spatial parameterization techniques combine PEST with stochastic field generation to efficiently explore pre- and post-calibration parameter and predictive uncertainty conduct parallel model optimization runs across PC or UNIX networks compare the worth of different proposed data acquisition strategies in reducing model predictive uncertainty, thereby optimizing resources allocated to such tasks quantify the contributions to model predictive uncertainty made by different parameter types establish the irreducible uncertainty of a model prior to calibrating that model quantify the reduction in predictive uncertainty accrued through model calibration use a model for quantitative hypothesis-testing test the contributions that model defects and simplifications make to potential pre- and post-calibration predictive error provide risk analysis input to the decision-support process Traditional methods of parameter estimation and uncertainty analysis based on a handful of zones of assumed piecewise parameter constancy do not provide the flexibility needed to extract vital information from expensive data, or to quantify the benefits and risks associated with different environmental management options. Some of the advances made over the last few years in PEST to overcome these problems include: Combining regularized inversion with the use of pilot points as a spatial parameterization device Use of the unique and extremely efficient SVD-assist regularized inversion methodology which combines subspace and Tikhonov methods. Inversion can be carried out with modelrun efficiencies comparable with traditional approaches even though the model may use hundreds of parameters Combining regularized inversion with stochastic field generation to produce many different calibration-constrained parameter fields with high levels of model run efficiency. All of these

2 can then be used in making any model prediction in order to explore the uncertainty of that prediction Use of Pareto methods to compare the merits of different environmental management strategies and implement model-based hypothesis testing Use of surrogate simple models or polynomial proxy models to increase the efficiency of the model calibration/uncertainty analysis processes, and/or accommodate difficult model numerical behaviour Deployment of linear methods to rapidly assess the relative worth of different strategies for future data acquisition PEST is accompanied by a large number of utility programs which support its use with commonly used models (such as MODFLOW-USG, FEFLOW, MODFLOW, MT3D, SEAWAT, TOUGH and others). This software automates PEST file set-up for complex parameter estimation and uncertainty analysis problems involving one or multiple models. Other members of the PEST suite implement the global SCE and CMA optimisation algorithms which can be employed where high levels of model nonlinearity and/or poor model numerical behaviour make use of gradient based inversion methods difficult. Detailed Workshop PROGRAM The course is taught over four days. The program for most days is a combination of lectures and practical sessions (hands on exercises). The preliminary program is found below. The content of practical sessions can be defined by individual students. A wide range of workshops is provided with the course. However students are welcome to bring their own models to use with PEST. If they do this, help in linking their models to PEST will be provided. Morning of day1: Overview of linear analysis and matrix algebra Description of matrices and vectors Matrix multiplication Subspaces Projection operators Orthogonality and orthogonal projections The null space Singular value decomposition Eigenvalues and eigenvectors Random vectors Covariance matrices

3 Principal component analysis Afternoon of day 1: Models and Decision Support: what models can and cannot achieve An overview of modelling Sources of predictive uncertainty - Bayes equation The role of risk in decision-making The role of uncertainty analysis in establishing risk What a simple model can achieve What a complex model can achieve The effects of model imperfections on model predictions The metrics for good modelling practice What calibration does for a model What calibration does not do for a model When bad models can make good predictions When good models make bad predictions Basics of uncertainty analysis Model-based hypothesis-testing Using models to encapsulate what we know Using models to quantify what we don t know Day 2: Basics of parameter estimation - theory and practice Nonlinear parameter estimation for well-posed inverse problems Application of nonlinear parameter estimation to model calibration Parameter correlation and non-uniqueness Formulation and minimization of an objective function Analysis of residuals The Jacobian matrix Parallelization of model runs Useful statistics arising from the parameter estimation process The nuts and bolts of using PEST

4 Building a PEST input dataset Analysis of PEST outputs Parameter non-uniqueness Parallel PEST and BeoPEST Morning of day 3: applied groundwater parameter estimation Multi-layered models Steady-state and transient calibration Utility support software supplied with PEST Avoiding structural noise caused by model inadequacies Temporal and spatial differencing in formulation of an objective function Problems associated with traditional approach to calibration of groundwater models Afternoon of day 3: highly parameterized inversion The benefits of highly parameterized inversion Tikhonov regularization Measurement and regularisation objective functions Pilot points as a parameterization device Singular value decomposition Solution subspace and null subspace Use of super parameters and SVD-assist for efficient inversion Difference between error and uncertainty Some case histories Morning of day 4: calibration-constrained uncertainty analysis Non-uniqueness and uncertainty Traditional uncertainty analysis Benefits of highly parameterized uncertainty analysis Linear error and uncertainty analysis

5 Nonlinear error and uncertainty analysis Null space Monte Carlo Pareto analysis and model-based hypothesis testing Assessment of the worth of new and existing data Paired simple and complex models Practical examples and demonstrations Afternoon of day 4: stochastic field generation The inherently stochastic nature of expert knowledge Brief overview of geostatistics Gaussian methods Multiple point geostatisics Other method of stochastic field generation Constraining parameter fields to respect calibration constraints What Participants will Receive Participants will receive a memory stick containing the following: Latest version of PEST Latest version of all PEST support utilities (over 200 programs) Copies of files and documentation for over 12 PEST workshops Literature (mainly published papers) on the use of PEST Copies of all slides used in the workshop Course Attendee Information Practitioners from a wide range of backgrounds will benefit from this course, whether new to PEST or with previous PEST experience. To get the most out of the course, attendees should have modelling experience, preferably in the groundwater or surface water disciplines. However the material presented on the afternoon of the first day will also benefit those who rely on models for decision-making purposes and need to have a better understanding of what models can and cannot achieve, but do not necessarily build them. This session will be open to the public for free. Hands-on labs are GUI-independent and cover a variety of modelling disciplines, so that anyone interested in model calibration, parameter estimation, or the analysis of numerical model

6 uncertainty can benefit from the course. Experience in working at the command line level is an advantage in doing the workshops. Make sure to bring your laptop so that we can install the software and workshops on it. Even if you are not a modeller you can still benefit from the course because the concepts explained in this course are vital to an understanding of the role of modelling in environmental management. They will also allow you to understand what models can and cannot do, and to separate fact from fiction in promises made by those who sell expensive models.

MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo I. Use PEST to create multiple calibrated MODFLOW simulations

MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo I. Use PEST to create multiple calibrated MODFLOW simulations v. 10.1 GMS 10.1 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo I Use PEST to create multiple calibrated MODFLOW simulations Objectives The PEST Null Space Monte Carlo modeling option

More information

GMS 9.0 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo I Use PEST to create multiple calibrated MODFLOW simulations

GMS 9.0 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo I Use PEST to create multiple calibrated MODFLOW simulations v. 9.0 GMS 9.0 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo I Use PEST to create multiple calibrated MODFLOW simulations Objectives The PEST Null Space Monte Carlo modeling option

More information

v GMS 10.0 Tutorial MODFLOW Advanced PEST Pilot Points, SVD-Assist, Parallel PEST Prerequisite Tutorials MODFLOW PEST Pilot Points

v GMS 10.0 Tutorial MODFLOW Advanced PEST Pilot Points, SVD-Assist, Parallel PEST Prerequisite Tutorials MODFLOW PEST Pilot Points v. 10.0 GMS 10.0 Tutorial Pilot Points, SVD-Assist, Parallel PEST Objectives Learn how to parameterize a MODFLOW model and run PEST to obtain optimal parameter values. Experiment with truncated singular

More information

MODFLOW Advanced PEST: SVD, SVD-Assist, Parallel PEST

MODFLOW Advanced PEST: SVD, SVD-Assist, Parallel PEST GMS 7.0 TUTORIALS MODFLOW Advanced PEST: SVD, SVD-Assist, Parallel PEST 1 Introduction The MODFLOW-Automated Parameter Estimation tutorial describes the basic functionality of PEST provided in GMS. It

More information

New Approaches for EEG Source Localization and Dipole Moment Estimation. Shun Chi Wu, Yuchen Yao, A. Lee Swindlehurst University of California Irvine

New Approaches for EEG Source Localization and Dipole Moment Estimation. Shun Chi Wu, Yuchen Yao, A. Lee Swindlehurst University of California Irvine New Approaches for EEG Source Localization and Dipole Moment Estimation Shun Chi Wu, Yuchen Yao, A. Lee Swindlehurst University of California Irvine Outline Motivation why EEG? Mathematical Model equivalent

More information

v. 8.0 GMS 8.0 Tutorial MODFLOW Advanced PEST SVD, SVD-Assist, Parallel PEST Prerequisite Tutorials None Time minutes

v. 8.0 GMS 8.0 Tutorial MODFLOW Advanced PEST SVD, SVD-Assist, Parallel PEST Prerequisite Tutorials None Time minutes v. 8.0 GMS 8.0 Tutorial MODFLOW Advanced PEST SVD, SVD-Assist, Parallel PEST Objectives Learn how to parameterize a MODFLOW model and run PEST to obtain optimal parameter values. Experiment with truncated

More information

Computationally Efficient Regularized Inversion for Highly Parameterized MODFLOW Models

Computationally Efficient Regularized Inversion for Highly Parameterized MODFLOW Models Computationally Efficient Regularized Inversion for Highly Parameterized MODFLOW Models Matthew Tonkin 1, Tom Clemo 2, John Doherty 3 1 S. S. Papadopulos & Associates, Inc., matt@sspa.com, Bethesda, MD,

More information

v MODFLOW Advanced PEST Pilot Points, SVD-Assist, Parallel PEST GMS Tutorials Time minutes

v MODFLOW Advanced PEST Pilot Points, SVD-Assist, Parallel PEST GMS Tutorials Time minutes v. 10.2 GMS 10.2 Tutorial Pilot Points, SVD-Assist, Parallel PEST Objectives Learn how to parameterize a MODFLOW model and run PEST to obtain optimal parameter values. Experiment with truncated singular

More information

PEST_HP PEST for Highly Parallelized Computing Environments Manual for Version 16

PEST_HP PEST for Highly Parallelized Computing Environments Manual for Version 16 PEST_HP PEST for Highly Parallelized Computing Environments Manual for Version 16 Watermark Numerical Computing January 2019 Table of Contents Table of Contents 1. Introduction... 1 1.1 General... 1 1.2

More information

Autonomous Mobile Robot Design

Autonomous Mobile Robot Design Autonomous Mobile Robot Design Topic: EKF-based SLAM Dr. Kostas Alexis (CSE) These slides have partially relied on the course of C. Stachniss, Robot Mapping - WS 2013/14 Autonomous Robot Challenges Where

More information

D025 Geostatistical Stochastic Elastic Iinversion - An Efficient Method for Integrating Seismic and Well Data Constraints

D025 Geostatistical Stochastic Elastic Iinversion - An Efficient Method for Integrating Seismic and Well Data Constraints D025 Geostatistical Stochastic Elastic Iinversion - An Efficient Method for Integrating Seismic and Well Data Constraints P.R. Williamson (Total E&P USA Inc.), A.J. Cherrett* (Total SA) & R. Bornard (CGGVeritas)

More information

PEST Model-Independent Parameter Estimation

PEST Model-Independent Parameter Estimation PEST Model-Independent Parameter Estimation User Manual: 5 th Edition 5th Edition published in 2005 (with slight additions in 2010) See also the Addendum to this manual for documenation of features of

More information

FMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu

FMA901F: Machine Learning Lecture 3: Linear Models for Regression. Cristian Sminchisescu FMA901F: Machine Learning Lecture 3: Linear Models for Regression Cristian Sminchisescu Machine Learning: Frequentist vs. Bayesian In the frequentist setting, we seek a fixed parameter (vector), with value(s)

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 Motivation The presence of uncertainties and disturbances has always been a vital issue in the control of dynamic systems. The classical linear controllers, PI and PID controllers

More information

INVERSE PROBLEMS IN GROUNDWATER MODELING

INVERSE PROBLEMS IN GROUNDWATER MODELING INVERSE PROBLEMS IN GROUNDWATER MODELING Theory and Applications of Transport in Porous Media Series Editor: Jacob Bear, Technion - Israel Institute of Technology, Haifa, Israel Volume 6 The titles published

More information

MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo II. Use results from PEST NSMC to evaluate the probability of a prediction

MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo II. Use results from PEST NSMC to evaluate the probability of a prediction v. 10.3 GMS 10.3 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo II Use results from PEST NSMC to evaluate the probability of a prediction Objectives Learn how to use the results from

More information

DISTRIBUTION STATEMENT A Approved for public release: distribution unlimited.

DISTRIBUTION STATEMENT A Approved for public release: distribution unlimited. AVIA Test Selection through Spatial Variance Bounding Method for Autonomy Under Test By Miles Thompson Senior Research Engineer Aerospace, Transportation, and Advanced Systems Lab DISTRIBUTION STATEMENT

More information

Bootstrapping Method for 14 June 2016 R. Russell Rhinehart. Bootstrapping

Bootstrapping Method for  14 June 2016 R. Russell Rhinehart. Bootstrapping Bootstrapping Method for www.r3eda.com 14 June 2016 R. Russell Rhinehart Bootstrapping This is extracted from the book, Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation,

More information

FEFLOW simulation of dynamic groundwater flow and solute transport

FEFLOW simulation of dynamic groundwater flow and solute transport FEFLOW simulation of dynamic groundwater flow and solute transport Post Doctoral Associate Aarhus University Research Foundation TATION presen Outline Scientific motivation The FEFLOW model The FEFLOW

More information

.. Lecture 2. learning and regularization. from interpolation to approximation.

.. Lecture 2. learning and regularization. from interpolation to approximation. .. Lecture. learning and regularization. from interpolation to approximation. Stéphane Canu and Cheng Soon Ong stephane.canu@insarouen.fr asi.insarouen.fr~scanu. RSISE ANU NICTA, Canberra INSA, Rouen RSISE,

More information

Feature selection. Term 2011/2012 LSI - FIB. Javier Béjar cbea (LSI - FIB) Feature selection Term 2011/ / 22

Feature selection. Term 2011/2012 LSI - FIB. Javier Béjar cbea (LSI - FIB) Feature selection Term 2011/ / 22 Feature selection Javier Béjar cbea LSI - FIB Term 2011/2012 Javier Béjar cbea (LSI - FIB) Feature selection Term 2011/2012 1 / 22 Outline 1 Dimensionality reduction 2 Projections 3 Attribute selection

More information

Robot Mapping. TORO Gradient Descent for SLAM. Cyrill Stachniss

Robot Mapping. TORO Gradient Descent for SLAM. Cyrill Stachniss Robot Mapping TORO Gradient Descent for SLAM Cyrill Stachniss 1 Stochastic Gradient Descent Minimize the error individually for each constraint (decomposition of the problem into sub-problems) Solve one

More information

GMS 9.1 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo II Use results from PEST NSMC to evaluate the probability of a prediction

GMS 9.1 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo II Use results from PEST NSMC to evaluate the probability of a prediction v. 9.1 GMS 9.1 Tutorial MODFLOW Stochastic Modeling, PEST Null Space Monte Carlo II Use results from PEST NSMC to evaluate the probability of a prediction Objectives Learn how to use the results from a

More information

1. Introduction. 2. Program structure. HYDROGNOMON components. Storage and data acquisition. Instruments and PYTHIA. Statistical

1. Introduction. 2. Program structure. HYDROGNOMON components. Storage and data acquisition. Instruments and PYTHIA. Statistical HYDROGNOMON: A HYDROLOGICAL DATA MANAGEMENT AND PROCESSING SOFTWARE TOOL European Geosciences Union (EGU) General Assembly, Vienna, Austria, 25-29 April 2005 Session HS29: Hydrological modelling software

More information

Linear Methods for Regression and Shrinkage Methods

Linear Methods for Regression and Shrinkage Methods Linear Methods for Regression and Shrinkage Methods Reference: The Elements of Statistical Learning, by T. Hastie, R. Tibshirani, J. Friedman, Springer 1 Linear Regression Models Least Squares Input vectors

More information

Ruch (Motion) Rozpoznawanie Obrazów Krzysztof Krawiec Instytut Informatyki, Politechnika Poznańska. Krzysztof Krawiec IDSS

Ruch (Motion) Rozpoznawanie Obrazów Krzysztof Krawiec Instytut Informatyki, Politechnika Poznańska. Krzysztof Krawiec IDSS Ruch (Motion) Rozpoznawanie Obrazów Krzysztof Krawiec Instytut Informatyki, Politechnika Poznańska 1 Krzysztof Krawiec IDSS 2 The importance of visual motion Adds entirely new (temporal) dimension to visual

More information

Modelling and Visualization of High Dimensional Data. Sample Examination Paper

Modelling and Visualization of High Dimensional Data. Sample Examination Paper Duration not specified UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE Modelling and Visualization of High Dimensional Data Sample Examination Paper Examination date not specified Time: Examination

More information

v Prerequisite Tutorials Required Components Time

v Prerequisite Tutorials Required Components Time v. 10.0 GMS 10.0 Tutorial MODFLOW Stochastic Modeling, Parameter Randomization Run MODFLOW in Stochastic (Monte Carlo) Mode by Randomly Varying Parameters Objectives Learn how to develop a stochastic (Monte

More information

Humanoid Robotics. Least Squares. Maren Bennewitz

Humanoid Robotics. Least Squares. Maren Bennewitz Humanoid Robotics Least Squares Maren Bennewitz Goal of This Lecture Introduction into least squares Use it yourself for odometry calibration, later in the lecture: camera and whole-body self-calibration

More information

CUE-10: Moderation Page 1. Comparative Usability Evaluation 10. Moderation. Observing usability test moderators

CUE-10: Moderation Page 1. Comparative Usability Evaluation 10. Moderation. Observing usability test moderators CUE-10: Moderation Page 1 Comparative Usability Evaluation 10 Moderation Observing usability test moderators Workshop: Boston, MA, USA, Wednesday 9 May 2018 CUE-10: Moderation Page 2 Call For Participation

More information

GMS 8.3 Tutorial MODFLOW Stochastic Modeling, Inverse Use PEST to calibrate multiple MODFLOW simulations using material sets

GMS 8.3 Tutorial MODFLOW Stochastic Modeling, Inverse Use PEST to calibrate multiple MODFLOW simulations using material sets v. 8.3 GMS 8.3 Tutorial Use PEST to calibrate multiple MODFLOW simulations using material sets Objectives The Stochastic inverse modeling option for MODFLOW is described. Multiple MODFLOW models with equally

More information

v MODFLOW Stochastic Modeling, Parameter Randomization GMS 10.3 Tutorial

v MODFLOW Stochastic Modeling, Parameter Randomization GMS 10.3 Tutorial v. 10.3 GMS 10.3 Tutorial MODFLOW Stochastic Modeling, Parameter Randomization Run MODFLOW in Stochastic (Monte Carlo) Mode by Randomly Varying Parameters Objectives Learn how to develop a stochastic (Monte

More information

WATER RESOURCES RESEARCH, VOL. 49, , doi: /wrcr.20064, 2013

WATER RESOURCES RESEARCH, VOL. 49, , doi: /wrcr.20064, 2013 WATER RESOURCES RESEARCH, VOL. 49, 536 553, doi:10.1002/wrcr.20064, 2013 Parameter estimation and predictive uncertainty in stochastic inverse modeling of groundwater flow: Comparing null-space Monte Carlo

More information

Optimum Array Processing

Optimum Array Processing Optimum Array Processing Part IV of Detection, Estimation, and Modulation Theory Harry L. Van Trees WILEY- INTERSCIENCE A JOHN WILEY & SONS, INC., PUBLICATION Preface xix 1 Introduction 1 1.1 Array Processing

More information

I How does the formulation (5) serve the purpose of the composite parameterization

I How does the formulation (5) serve the purpose of the composite parameterization Supplemental Material to Identifying Alzheimer s Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis I How does the formulation (5)

More information

Multiple-Choice Questionnaire Group C

Multiple-Choice Questionnaire Group C Family name: Vision and Machine-Learning Given name: 1/28/2011 Multiple-Choice naire Group C No documents authorized. There can be several right answers to a question. Marking-scheme: 2 points if all right

More information

Contents. I Basics 1. Copyright by SIAM. Unauthorized reproduction of this article is prohibited.

Contents. I Basics 1. Copyright by SIAM. Unauthorized reproduction of this article is prohibited. page v Preface xiii I Basics 1 1 Optimization Models 3 1.1 Introduction... 3 1.2 Optimization: An Informal Introduction... 4 1.3 Linear Equations... 7 1.4 Linear Optimization... 10 Exercises... 12 1.5

More information

Application of LSQR to Calibration of a MODFLOW Model: A Synthetic Study

Application of LSQR to Calibration of a MODFLOW Model: A Synthetic Study Application of LSQR to Calibration of a MODFLOW Model: A Synthetic Study Chris Muffels 1,2, Matthew Tonkin 2,3, Haijiang Zhang 1, Mary Anderson 1, Tom Clemo 4 1 University of Wisconsin-Madison, muffels@geology.wisc.edu,

More information

MODFLOW PEST Pilot Points Use pilot points with PEST to automatically calibrate a MODFLOW model

MODFLOW PEST Pilot Points Use pilot points with PEST to automatically calibrate a MODFLOW model v. 10.2 GMS 10.2 Tutorial Use pilot points with PEST to automatically calibrate a MODFLOW model Objectives Learn the features and options related to pilot points when used with PEST. Use fixed value pilot

More information

Design of Experiments

Design of Experiments Seite 1 von 1 Design of Experiments Module Overview In this module, you learn how to create design matrices, screen factors, and perform regression analysis and Monte Carlo simulation using Mathcad. Objectives

More information

A Fast CMS Technique for Computational Efficient System Re-analyses in Structural Dynamics

A Fast CMS Technique for Computational Efficient System Re-analyses in Structural Dynamics Paper 23 A Fast CMS Technique for Computational Efficient System Re-analyses in Structural Dynamics D.-C. Papadioti and C. Papadimitriou Department of Mechanical Engineering University of Thessaly, Volos,

More information

Online Subspace Estimation and Tracking from Missing or Corrupted Data

Online Subspace Estimation and Tracking from Missing or Corrupted Data Online Subspace Estimation and Tracking from Missing or Corrupted Data Laura Balzano www.ece.wisc.edu/~sunbeam Work with Benjamin Recht and Robert Nowak Subspace Representations Capture Dependencies Subspace

More information

PARAMETERIZATION AND SAMPLING DESIGN FOR WATER NETWORKS DEMAND CALIBRATION USING THE SINGULAR VALUE DECOMPOSITION: APPLICATION TO A REAL NETWORK

PARAMETERIZATION AND SAMPLING DESIGN FOR WATER NETWORKS DEMAND CALIBRATION USING THE SINGULAR VALUE DECOMPOSITION: APPLICATION TO A REAL NETWORK 11 th International Conference on Hydroinformatics HIC 2014, New York City, USA PARAMETERIZATION AND SAMPLING DESIGN FOR WATER NETWORKS DEMAND CALIBRATION USING THE SINGULAR VALUE DECOMPOSITION: APPLICATION

More information

The Anatomical Equivalence Class Formulation and its Application to Shape-based Computational Neuroanatomy

The Anatomical Equivalence Class Formulation and its Application to Shape-based Computational Neuroanatomy The Anatomical Equivalence Class Formulation and its Application to Shape-based Computational Neuroanatomy Sokratis K. Makrogiannis, PhD From post-doctoral research at SBIA lab, Department of Radiology,

More information

All images are degraded

All images are degraded Lecture 7 Image Relaxation: Restoration and Feature Extraction ch. 6 of Machine Vision by Wesley E. Snyder & Hairong Qi Spring 2018 16-725 (CMU RI) : BioE 2630 (Pitt) Dr. John Galeotti The content of these

More information

Inclusion of Aleatory and Epistemic Uncertainty in Design Optimization

Inclusion of Aleatory and Epistemic Uncertainty in Design Optimization 10 th World Congress on Structural and Multidisciplinary Optimization May 19-24, 2013, Orlando, Florida, USA Inclusion of Aleatory and Epistemic Uncertainty in Design Optimization Sirisha Rangavajhala

More information

Model Reduction for Variable-Fidelity Optimization Frameworks

Model Reduction for Variable-Fidelity Optimization Frameworks Model Reduction for Variable-Fidelity Optimization Frameworks Karen Willcox Aerospace Computational Design Laboratory Department of Aeronautics and Astronautics Massachusetts Institute of Technology Workshop

More information

Geometric camera models and calibration

Geometric camera models and calibration Geometric camera models and calibration http://graphics.cs.cmu.edu/courses/15-463 15-463, 15-663, 15-862 Computational Photography Fall 2018, Lecture 13 Course announcements Homework 3 is out. - Due October

More information

Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques

Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques Analysis of Functional MRI Timeseries Data Using Signal Processing Techniques Sea Chen Department of Biomedical Engineering Advisors: Dr. Charles A. Bouman and Dr. Mark J. Lowe S. Chen Final Exam October

More information

Multidisciplinary System Design Optimization (MSDO) Course Summary

Multidisciplinary System Design Optimization (MSDO) Course Summary Multidisciplinary System Design Optimization (MSDO) Course Summary Lecture 23 Prof. Olivier de Weck Prof. Karen Willcox 1 Outline Summarize course content Present some emerging research directions Interactive

More information

m=[a,b,c,d] T together with the a posteriori covariance

m=[a,b,c,d] T together with the a posteriori covariance zimuthal VO analysis: stabilizing the model parameters Chris Davison*, ndrew Ratcliffe, Sergio Grion (CGGVeritas), Rodney Johnston, Carlos Duque, Musa Maharramov (BP). solved using linear least squares

More information

Tensor Based Approaches for LVA Field Inference

Tensor Based Approaches for LVA Field Inference Tensor Based Approaches for LVA Field Inference Maksuda Lillah and Jeff Boisvert The importance of locally varying anisotropy (LVA) in model construction can be significant; however, it is often ignored

More information

Image Analysis, Classification and Change Detection in Remote Sensing

Image Analysis, Classification and Change Detection in Remote Sensing Image Analysis, Classification and Change Detection in Remote Sensing WITH ALGORITHMS FOR ENVI/IDL Morton J. Canty Taylor &. Francis Taylor & Francis Group Boca Raton London New York CRC is an imprint

More information

v GMS 10.0 Tutorial MODFLOW PEST Transient Pump Test Calibration Tools for calibrating transient MODFLOW models

v GMS 10.0 Tutorial MODFLOW PEST Transient Pump Test Calibration Tools for calibrating transient MODFLOW models v. 10.0 GMS 10.0 Tutorial MODFLOW PEST Transient Pump Test Calibration Tools for calibrating transient MODFLOW models Objectives Learn how to setup a transient simulation, import transient observation

More information

Practical Course WS12/13 Introduction to Monte Carlo Localization

Practical Course WS12/13 Introduction to Monte Carlo Localization Practical Course WS12/13 Introduction to Monte Carlo Localization Cyrill Stachniss and Luciano Spinello 1 State Estimation Estimate the state of a system given observations and controls Goal: 2 Bayes Filter

More information

Adaptive spatial resampling as a Markov chain Monte Carlo method for uncertainty quantification in seismic reservoir characterization

Adaptive spatial resampling as a Markov chain Monte Carlo method for uncertainty quantification in seismic reservoir characterization 1 Adaptive spatial resampling as a Markov chain Monte Carlo method for uncertainty quantification in seismic reservoir characterization Cheolkyun Jeong, Tapan Mukerji, and Gregoire Mariethoz Department

More information

Robustness analysis of metal forming simulation state of the art in practice. Lectures. S. Wolff

Robustness analysis of metal forming simulation state of the art in practice. Lectures. S. Wolff Lectures Robustness analysis of metal forming simulation state of the art in practice S. Wolff presented at the ICAFT-SFU 2015 Source: www.dynardo.de/en/library Robustness analysis of metal forming simulation

More information

Calibration and emulation of TIE-GCM

Calibration and emulation of TIE-GCM Calibration and emulation of TIE-GCM Serge Guillas School of Mathematics Georgia Institute of Technology Jonathan Rougier University of Bristol Big Thanks to Crystal Linkletter (SFU-SAMSI summer school)

More information

Statistics (STAT) Statistics (STAT) 1. Prerequisites: grade in C- or higher in STAT 1200 or STAT 1300 or STAT 1400

Statistics (STAT) Statistics (STAT) 1. Prerequisites: grade in C- or higher in STAT 1200 or STAT 1300 or STAT 1400 Statistics (STAT) 1 Statistics (STAT) STAT 1200: Introductory Statistical Reasoning Statistical concepts for critically evaluation quantitative information. Descriptive statistics, probability, estimation,

More information

ICRA 2016 Tutorial on SLAM. Graph-Based SLAM and Sparsity. Cyrill Stachniss

ICRA 2016 Tutorial on SLAM. Graph-Based SLAM and Sparsity. Cyrill Stachniss ICRA 2016 Tutorial on SLAM Graph-Based SLAM and Sparsity Cyrill Stachniss 1 Graph-Based SLAM?? 2 Graph-Based SLAM?? SLAM = simultaneous localization and mapping 3 Graph-Based SLAM?? SLAM = simultaneous

More information

Modeling and Reasoning with Bayesian Networks. Adnan Darwiche University of California Los Angeles, CA

Modeling and Reasoning with Bayesian Networks. Adnan Darwiche University of California Los Angeles, CA Modeling and Reasoning with Bayesian Networks Adnan Darwiche University of California Los Angeles, CA darwiche@cs.ucla.edu June 24, 2008 Contents Preface 1 1 Introduction 1 1.1 Automated Reasoning........................

More information

How to Win With Non-Gaussian Data: Poisson Imaging Version

How to Win With Non-Gaussian Data: Poisson Imaging Version Comments On How to Win With Non-Gaussian Data: Poisson Imaging Version Alanna Connors David A. van Dyk a Department of Statistics University of California, Irvine a Joint Work with James Chaing and the

More information

MODFLOW PEST Transient Pump Test Calibration Tools for calibrating transient MODFLOW models

MODFLOW PEST Transient Pump Test Calibration Tools for calibrating transient MODFLOW models v. 10.2 GMS 10.2 Tutorial Tools for calibrating transient MODFLOW models Objectives Learn how to setup a transient simulation, import transient observation data, and use PEST to calibrate the model. Prerequisite

More information

Laboratorio di Problemi Inversi Esercitazione 3: regolarizzazione iterativa, metodo di Landweber

Laboratorio di Problemi Inversi Esercitazione 3: regolarizzazione iterativa, metodo di Landweber Laboratorio di Problemi Inversi Esercitazione 3: regolarizzazione iterativa, metodo di Landweber Luca Calatroni Dipartimento di Matematica, Universitá degli studi di Genova May 18, 2016. Luca Calatroni

More information

A Novel Image Super-resolution Reconstruction Algorithm based on Modified Sparse Representation

A Novel Image Super-resolution Reconstruction Algorithm based on Modified Sparse Representation , pp.162-167 http://dx.doi.org/10.14257/astl.2016.138.33 A Novel Image Super-resolution Reconstruction Algorithm based on Modified Sparse Representation Liqiang Hu, Chaofeng He Shijiazhuang Tiedao University,

More information

Advanced Tester Certification Test Manager

Advanced Tester Certification Test Manager Home > Advanced Tester Certification Test Manager Advanced Tester Certification Test Manager Accredited training for the ISTQB Advanced Tester Certification Test Manager (CTAL- TM) certification. This

More information

Louis Fourrier Fabien Gaie Thomas Rolf

Louis Fourrier Fabien Gaie Thomas Rolf CS 229 Stay Alert! The Ford Challenge Louis Fourrier Fabien Gaie Thomas Rolf Louis Fourrier Fabien Gaie Thomas Rolf 1. Problem description a. Goal Our final project is a recent Kaggle competition submitted

More information

Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11 th, 2011

Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11 th, 2011 Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11 th, 2011 1. Introduction Reddit is one of the most popular online social news websites with millions

More information

Advanced Techniques for Mobile Robotics Graph-based SLAM using Least Squares. Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz

Advanced Techniques for Mobile Robotics Graph-based SLAM using Least Squares. Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz Advanced Techniques for Mobile Robotics Graph-based SLAM using Least Squares Wolfram Burgard, Cyrill Stachniss, Kai Arras, Maren Bennewitz SLAM Constraints connect the poses of the robot while it is moving

More information

Dimensionality Reduction, including by Feature Selection.

Dimensionality Reduction, including by Feature Selection. Dimensionality Reduction, including by Feature Selection www.cs.wisc.edu/~dpage/cs760 Goals for the lecture you should understand the following concepts filtering-based feature selection information gain

More information

Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision Multiple View Geometry in Computer Vision Prasanna Sahoo Department of Mathematics University of Louisville 1 Structure Computation Lecture 18 March 22, 2005 2 3D Reconstruction The goal of 3D reconstruction

More information

Predictive uncertainty analysis of a saltwater intrusion model using null space Monte Carlo

Predictive uncertainty analysis of a saltwater intrusion model using null space Monte Carlo WATER RESOURCES RESEARCH, VOL. 47,, doi:10.1029/2010wr009342, 2011 Predictive uncertainty analysis of a saltwater intrusion model using null space Monte Carlo Daan Herckenrath, 1 Christian D. Langevin,

More information

Model parametrization strategies for Newton-based acoustic full waveform

Model parametrization strategies for Newton-based acoustic full waveform Model parametrization strategies for Newton-based acoustic full waveform inversion Amsalu Y. Anagaw, University of Alberta, Edmonton, Canada, aanagaw@ualberta.ca Summary This paper studies the effects

More information

Quasi-Monte Carlo Methods Combating Complexity in Cost Risk Analysis

Quasi-Monte Carlo Methods Combating Complexity in Cost Risk Analysis Quasi-Monte Carlo Methods Combating Complexity in Cost Risk Analysis Blake Boswell Booz Allen Hamilton ISPA / SCEA Conference Albuquerque, NM June 2011 1 Table Of Contents Introduction Monte Carlo Methods

More information

COMP 558 lecture 19 Nov. 17, 2010

COMP 558 lecture 19 Nov. 17, 2010 COMP 558 lecture 9 Nov. 7, 2 Camera calibration To estimate the geometry of 3D scenes, it helps to know the camera parameters, both external and internal. The problem of finding all these parameters is

More information

We deliver Global Engineering Solutions. Efficiently. This page contains no technical data Subject to the EAR or the ITAR

We deliver Global Engineering Solutions. Efficiently. This page contains no technical data Subject to the EAR or the ITAR Numerical Computation, Statistical analysis and Visualization Using MATLAB and Tools Authors: Jamuna Konda, Jyothi Bonthu, Harpitha Joginipally Infotech Enterprises Ltd, Hyderabad, India August 8, 2013

More information

1. Introduction. 2. Modelling elements III. CONCEPTS OF MODELLING. - Models in environmental sciences have five components:

1. Introduction. 2. Modelling elements III. CONCEPTS OF MODELLING. - Models in environmental sciences have five components: III. CONCEPTS OF MODELLING 1. INTRODUCTION 2. MODELLING ELEMENTS 3. THE MODELLING PROCEDURE 4. CONCEPTUAL MODELS 5. THE MODELLING PROCEDURE 6. SELECTION OF MODEL COMPLEXITY AND STRUCTURE 1 1. Introduction

More information

Pearson BTEC Level 5 Higher National Diploma in Engineering (Electrical and Electronic Engineering)

Pearson BTEC Level 5 Higher National Diploma in Engineering (Electrical and Electronic Engineering) Pearson BTEC Programme Pearson BTEC Level 5 Higher National Diploma in Engineering (Electrical and Electronic Engineering) Code: BHNDE5 Guided Learning Hours: 960 Hours Programme Structure: The Higher

More information

Dimension Reduction CS534

Dimension Reduction CS534 Dimension Reduction CS534 Why dimension reduction? High dimensionality large number of features E.g., documents represented by thousands of words, millions of bigrams Images represented by thousands of

More information

The Curse of Dimensionality. Panagiotis Parchas Advanced Data Management Spring 2012 CSE HKUST

The Curse of Dimensionality. Panagiotis Parchas Advanced Data Management Spring 2012 CSE HKUST The Curse of Dimensionality Panagiotis Parchas Advanced Data Management Spring 2012 CSE HKUST Multiple Dimensions As we discussed in the lectures, many times it is convenient to transform a signal(time

More information

Lecture 8: Registration

Lecture 8: Registration ME 328: Medical Robotics Winter 2019 Lecture 8: Registration Allison Okamura Stanford University Updates Assignment 4 Sign up for teams/ultrasound by noon today at: https://tinyurl.com/me328-uslab Main

More information

Spatial Variation of Sea-Level Sea level reconstruction

Spatial Variation of Sea-Level Sea level reconstruction Spatial Variation of Sea-Level Sea level reconstruction Biao Chang Multimedia Environmental Simulation Laboratory School of Civil and Environmental Engineering Georgia Institute of Technology Advisor:

More information

Face Recognition using Eigenfaces SMAI Course Project

Face Recognition using Eigenfaces SMAI Course Project Face Recognition using Eigenfaces SMAI Course Project Satarupa Guha IIIT Hyderabad 201307566 satarupa.guha@research.iiit.ac.in Ayushi Dalmia IIIT Hyderabad 201307565 ayushi.dalmia@research.iiit.ac.in Abstract

More information

IMAGE ANALYSIS, CLASSIFICATION, and CHANGE DETECTION in REMOTE SENSING

IMAGE ANALYSIS, CLASSIFICATION, and CHANGE DETECTION in REMOTE SENSING SECOND EDITION IMAGE ANALYSIS, CLASSIFICATION, and CHANGE DETECTION in REMOTE SENSING ith Algorithms for ENVI/IDL Morton J. Canty с*' Q\ CRC Press Taylor &. Francis Group Boca Raton London New York CRC

More information

Model Based Perspective Inversion

Model Based Perspective Inversion Model Based Perspective Inversion A. D. Worrall, K. D. Baker & G. D. Sullivan Intelligent Systems Group, Department of Computer Science, University of Reading, RG6 2AX, UK. Anthony.Worrall@reading.ac.uk

More information

MODEL ANALYSIS AND DECISION SUPPORT (MADS) FOR COMPLEX PHYSICS MODELS

MODEL ANALYSIS AND DECISION SUPPORT (MADS) FOR COMPLEX PHYSICS MODELS XIX International Conference on Water Resources CMWR 2012 University of Illinois at Urbana-Champaign June 17-22, 2012 MODEL ANALYSIS AND DECISION SUPPORT (MADS) FOR COMPLEX PHYSICS MODELS Velimir V. Vesselinov

More information

Theoretical Concepts of Machine Learning

Theoretical Concepts of Machine Learning Theoretical Concepts of Machine Learning Part 2 Institute of Bioinformatics Johannes Kepler University, Linz, Austria Outline 1 Introduction 2 Generalization Error 3 Maximum Likelihood 4 Noise Models 5

More information

8 Modeling Fractured Rock

8 Modeling Fractured Rock 8 Modeling Fractured Rock Groundwater flow and chemical transport models can help to characterize and remediate fractured rock sites at all scales. It is critical that any modeling be performed by modelers

More information

CLOSING REGIONAL WORKSHOP FOR THE SEEA PROJECT

CLOSING REGIONAL WORKSHOP FOR THE SEEA PROJECT CLOSING REGIONAL WORKSHOP FOR THE SEEA PROJECT Development of Pilot Physical Supply and Use Tables for Water in Malaysia Mohd Yusof Saari Universiti Putra Malaysia Date : 28-30 November 2017 Time : 9:30

More information

ECS289: Scalable Machine Learning

ECS289: Scalable Machine Learning ECS289: Scalable Machine Learning Cho-Jui Hsieh UC Davis Sept 22, 2016 Course Information Website: http://www.stat.ucdavis.edu/~chohsieh/teaching/ ECS289G_Fall2016/main.html My office: Mathematical Sciences

More information

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS /$ IEEE

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS /$ IEEE IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 1 Exploration of Heterogeneous FPGAs for Mapping Linear Projection Designs Christos-S. Bouganis, Member, IEEE, Iosifina Pournara, and Peter

More information

CS 664 Structure and Motion. Daniel Huttenlocher

CS 664 Structure and Motion. Daniel Huttenlocher CS 664 Structure and Motion Daniel Huttenlocher Determining 3D Structure Consider set of 3D points X j seen by set of cameras with projection matrices P i Given only image coordinates x ij of each point

More information

Lecture 3: Camera Calibration, DLT, SVD

Lecture 3: Camera Calibration, DLT, SVD Computer Vision Lecture 3 23--28 Lecture 3: Camera Calibration, DL, SVD he Inner Parameters In this section we will introduce the inner parameters of the cameras Recall from the camera equations λx = P

More information

Discovery of the Source of Contaminant Release

Discovery of the Source of Contaminant Release Discovery of the Source of Contaminant Release Devina Sanjaya 1 Henry Qin Introduction Computer ability to model contaminant release events and predict the source of release in real time is crucial in

More information

PERFORMANCE OF THE DISTRIBUTED KLT AND ITS APPROXIMATE IMPLEMENTATION

PERFORMANCE OF THE DISTRIBUTED KLT AND ITS APPROXIMATE IMPLEMENTATION 20th European Signal Processing Conference EUSIPCO 2012) Bucharest, Romania, August 27-31, 2012 PERFORMANCE OF THE DISTRIBUTED KLT AND ITS APPROXIMATE IMPLEMENTATION Mauricio Lara 1 and Bernard Mulgrew

More information

Robot Mapping. Least Squares Approach to SLAM. Cyrill Stachniss

Robot Mapping. Least Squares Approach to SLAM. Cyrill Stachniss Robot Mapping Least Squares Approach to SLAM Cyrill Stachniss 1 Three Main SLAM Paradigms Kalman filter Particle filter Graphbased least squares approach to SLAM 2 Least Squares in General Approach for

More information

Graphbased. Kalman filter. Particle filter. Three Main SLAM Paradigms. Robot Mapping. Least Squares Approach to SLAM. Least Squares in General

Graphbased. Kalman filter. Particle filter. Three Main SLAM Paradigms. Robot Mapping. Least Squares Approach to SLAM. Least Squares in General Robot Mapping Three Main SLAM Paradigms Least Squares Approach to SLAM Kalman filter Particle filter Graphbased Cyrill Stachniss least squares approach to SLAM 1 2 Least Squares in General! Approach for

More information

Feature Selection Using Modified-MCA Based Scoring Metric for Classification

Feature Selection Using Modified-MCA Based Scoring Metric for Classification 2011 International Conference on Information Communication and Management IPCSIT vol.16 (2011) (2011) IACSIT Press, Singapore Feature Selection Using Modified-MCA Based Scoring Metric for Classification

More information

Gaussian Processes for Robotics. McGill COMP 765 Oct 24 th, 2017

Gaussian Processes for Robotics. McGill COMP 765 Oct 24 th, 2017 Gaussian Processes for Robotics McGill COMP 765 Oct 24 th, 2017 A robot must learn Modeling the environment is sometimes an end goal: Space exploration Disaster recovery Environmental monitoring Other

More information

Camera calibration. Robotic vision. Ville Kyrki

Camera calibration. Robotic vision. Ville Kyrki Camera calibration Robotic vision 19.1.2017 Where are we? Images, imaging Image enhancement Feature extraction and matching Image-based tracking Camera models and calibration Pose estimation Motion analysis

More information