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When you are working with large and complex Simulink models, it is sometimes difficult to determine which model parameters impact behavior the most. Using Monte Carlo simulations, correlation techniques and design of experiments (DoE), Sensitivity Analysis allows you to determine which parameters have the greatest impact on your model.
Views: 2830 Opti-Num Solutions

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Views: 3491 MATLAB

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Download scripts/files/codes and further information: www.gamsoptimization.com. This video shows a small sensitivity analysis to point out how a big one can work.
Views: 1230 man goon

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Dynamic optimization solutions may be sensitive to certain parameters or variables that are decisions. A sensitivity analysis determines how the objective or other variables change with those parameters or decision variables.
Views: 1345 APMonitor.com

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Download scripts/files/codes and further information: www.gamsoptimization.com. This video introduces to my youtube channel and website. I explain how to link GAMS, Matlab and Excel so that sensitivity analyses can be conducted conveniently.
Views: 1633 man goon

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The concept of sensitivity of a function to small changes in one of its parameters is introduced. After showing how to compute it, a few examples are considered. The final example shows how sensitivity can be applied to closed loop control design.
Views: 26907 Gordon Parker

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Views: 901 Mark Somerville

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Learn more about Simulink Design Optimization: http://goo.gl/X8fhSi Download a free trial of Simulink: https://goo.gl/mEl9ym Analyze the behavior of an electrical circuit used to rectify an AC voltage supply and then amplify it using an op-amp. Use the Sensitivity Analysis tool in Simulink Design Optimization™ to identify which circuit parameters have the greatest impact on characteristics such as the maximum voltage value and waveform smoothness. Sensitivity Analysis Tool Explore design space and determine the most influential model parameters The Sensitivity Analysis tool lets you explore the design space and determine the most influential Simulink® model parameters using design of experiments, Monte Carlo simulations, and correlation analysis. Using this tool, you can: -Select and sample parameters using design of experiments. -Specify design requirements. -Perform Monte Carlo simulations to evaluate the design requirement at selected parameter values. -Analyze and visualize model sensitivity to parameters. -You can accelerate evaluation of design requirements using parallel computing and Simulink fast restart.
Views: 2241 MATLAB

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The JRC's Sensitivity Analysis group (SAMO) presents "A New Framework for Comprehensive, Efficient, and Robust Global Sensitivity Analysis", by Saman Razavi, University of Saskatchewan. Seminar at the European Commision Joint Research Centre (JRC) – Ispra – 2 May 2017. This presentation provides an overview of the theory and application of a new framework for Global Sensitivity Analysis (GSA), called Variogram Analysis of Response Surfaces (VARS). VARS utilizes the concepts of variograms and covariograms to characterize a spectrum of sensitivity-related information across the model factor space. VARS is a general framework with explicit theoretical relationships with variance-based (e.g., Sobol) and derivative-based (e.g, Morris) approaches to GSA, while being highly efficient and statistically robust. This presentation also discusses strategies for improved convergence and robustness of GSA, and to this end, introduces a sequential sampling algorithm, called Progressive Latin Hypercube Sampling (PLHS), which allows progressively increasing the sample size, while maintaining the required distributional properties. Saman Razavi received his PhD degree (2013) in civil engineering from the University of Waterloo, Ontario, and his MSc (2004) and BSc (2002) degrees in civil engineering from Amirkabir University and Iran University of Science and Technology in Iran. His research interests include environmental and water resources systems analysis, hydrologic modelling, single and multiple-objective optimization, sensitivity and uncertainty analysis, and climate change and impacts on hydrology and water resources. Seminar organiser: William BECKER Video recording, audio and video editing: Mayeul KAUFFMANN
Views: 1663 mayeulk

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Local sensitivity analysis
Views: 338 Jef Caers

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This video shows how to execute the sensitivity analsis for any input or output property

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NeuralTools has a new sensitivity feature which trains a number of neural nets to ensure good results and avoiding 'lucky' and 'unlucky' test cases.

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Views: 9846 Mark Somerville

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Views: 4280 techwinder

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Views: 1540 Wehrspohn

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Show an introduction to sensitivity analysis using the matrix form of the simplex method
Views: 2635 GOAL PROJECT

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All predictions and simulations from statistical models require an understanding of how to reflect uncertainty and utilise sensitivity analysis. This tutorial explains these concepts and highlights key points to consider.
Views: 5668 Alan Maloney

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Sobol' and regionalized sensitivity analysis
Views: 606 Jef Caers

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This video will define sensitivity for a feedback control system. It will derive the sensitivity function based upon parameter and block variations. It will also determine the sensitivity of the steady-state error due to a disturbance or sensor noise.
Views: 3121 Rose-Hulman Online

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Analyzes net present value using sensitivity analysis and generates a tornado plot. Made by faculty at the University of Colorado Boulder Department of Chemical & Biological Engineering. Check out our process design playlist: http://www.youtube.com/playlist?list=PL4xAk5aclnUjEuE_fvbyEts_oBpHYcwLY
Views: 65564 LearnChemE

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Machine Learning, Classification and Algorithms using MATLAB: Learn to Implement Classification Algorithms In One of the Most Power Tool used by Scientists and Engineer. This course is designed to cover one of the most interesting areas of machine learning called classification. I will take you step-by-step in this course and will first cover the basics of MATLAB. Following that we will look into the details of how to use different machine learning algorithms using MATLAB. Specifically, we will be looking at the MATLAB toolbox called statistic and machine learning toolbox.We will implement some of the most commonly used classification algorithms such as K-Nearest Neighbor, Naive Bayes, Discriminant Analysis, Decision Tress, Support Vector Machines, Error Correcting Output Codes and Ensembles. Following that we will be looking at how to cross validate these models and how to evaluate their performances. Intuition into the classification algorithms is also included so that a person with no mathematical background can still comprehend the essential ideas. The following are the course outlines. Segment 1: Grabbing and Importing Dataset + Segment 2: K-Nearest Neighbor + Segment 3: Naive Bayes + Segment 4: Decision Trees + Segment 5: Discriminant Analysis + Segment 6: Support Vector Machines + Segment 7: Error Correcting Output Codes + Segment 8: Classification with Ensembles + Segment 9: Validation Methods + Segment 10: Evaluating Performance.

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Sobol' sensitivity analysis for stochastic numerical codes, Iooss Bertrand

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In this lesson, we learn how to regenerate the final (optimal) Simplex table given the optimal set of basic decision variables and the initial Linear Programming problem.
Views: 69346 Shokoufeh Mirzaei

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This demo shows how to performance sensitivity analysis using SimLab 2.2 and jEPlus. An example project included in jEPlus v1.6's distribution package is used in this walk-through. (This video has no sound)
Views: 4286 jEPlusMedia

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What's so good about matrices? You can take advantage of everything you previously learned and make inference about functions just by knowing a derivative at a point. An example involving sensitivity of outputs with respect to inputs is included here.
Views: 240 Prof Ghrist Math

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Dr. Saman Razavi speaks about the fundamentals of global sensitivity analysis (GSA) and VARS, which is a new mathematical framework for GSA of computer simulation models, including Earth and Environmental Systems Models (EESMs). VARS, which stands for Variogram Analysis of Response Surfaces, utilizes directional variogram and covariogram functions to characterize the full spectrum of sensitivity-related information, thereby providing a comprehensive set of "global" sensitivity metrics with minimal computational cost. See more on VARS in http://homepage.usask.ca/~ser134/nex_gen_sen_an.php Dr. Saman Razavi leads Watershed Systems Analysis and Modelling Lab at the Global Institute for Water Security. He is an assistant professor with School of Environment and Sustainability and Department of Civil and Geological Engineering at the University of Saskatchewan. He received the PhD degree (2013) in civil engineering from the University of Waterloo, Ontario, and the MSc (2004) and BSc (2002) degrees in civil engineering from Amirkabir University and Iran University of Science and Technology in Iran. Dr. Razavi is an Associate Editor of Journal of Hydrology and an Editorial Board Member of Environmental Modelling & Software. He also serves on several international committees. His research interests include environmental and water resources systems analysis, hydrologic modelling, single- and multiple-objective optimization, sensitivity and uncertainty analysis, and climate change and impacts on hydrology and water resources. http://homepage.usask.ca/~ser134/ ________________________________________________________ What is Global Sensitivity Analysis (GSA)? Global sensitivity analysis is a systems theoretic approach to characterizing the overall (average) sensitivity of one or more model responses across the factor space, by attributing the variability of those responses to different controlling (but uncertain) factors (e.g., model parameters, forcings, and boundary and initial conditions). ________________________________________________________ What was the Motivation for the Development of VARS? VARS was developed to address two major issues with GSA: · Ambiguous Definition of "Global" Sensitivity: different GSA methods are based in different philosophies and theoretical definitions of sensitivity, leading to different, even conflicting, assessments of the underlying sensitivities for a given problem. · Computational Cost: the cost of carrying out GSA can be large, even excessive, for high-dimensional problems and/or computationally intensive models, where cost (or "efficiency") is commonly assessed by of the number of required model runs. ________________________________________________________ What are the Special Features of VARS? · VARS re-defines GSA by characterizing a comprehensive spectrum of information about the underlying sensitivities of a response surface to its factors, while reducing to well-known and commonly used approaches to GSA as special/limiting cases. · VARS generates a new set of sensitivity metrics called IVARS (Integrated Variogram Across a Range of Scales) that summarize the variance of change (rate of variability) in model response at a range of perturbation scales in the factor space. · VARS also generates the Sobol (variance-based) total-order effect, the most popular metric for GSA, and the Morris (derivative-based) elementary effects across the full range of step sizes in numerical differencing (theoretical relationship exists). · VARS is highly efficient and statistically robust, providing stable results within 1-2 orders of magnitude smaller numbers of sampled points (model runs), compared with alternative GSA approaches, such as the Sobol and Morris approaches. · VARS effectively and efficiently handles high-dimensional problems, because of its computational efficiency, which is, in part, due to VARS being based on the information contained in pairs of points, rather than in individual points. · VARS is unique in that it characterizes different sensitivity-related properties of response surfaces including local sensitivities and their global distribution, the global distribution of model responses, and the structure of the response surface. · VARS tackles the scale issue of sensitivity analysis by providing sensitivity information spanning a range of scales across the factor space, from small-scale features such as roughness/noise to large-scale features such as multimodality.

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Present challenges in sensitivity analysis, Andrea SALTELLI

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Views: 3526 Lou Gattis

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This video describes how to set up a parameter sensitivity analysis (PSA) in GastroPlus. This allows researchers to determine the effect of an input parameter, e.g., solubility, on a pharmacokinetic property like fraction absorbed. The tasks in Tutorial 5.3.7 will be illustrated.

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Sensitivity analysis should be a central part of the model development process, yet software to actually perform the best-practice approaches are seldom available. In this talk, there is justification for the importance of sensitivity analysis, step-by-step examples of how to use SALib and an outline of the advantages. Full details — http://london.pydata.org/schedule/presentation/45/
Views: 1735 PyData

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Nonlinear and linear differential equations are solved with numerical integrators in MATLAB. This tutorial compares a nonlinear and linear version of a Continuously Stirred Tank Reactor (CSTR) in MATLAB.
Views: 18248 APMonitor.com

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This Product Training webinar discusses sensitivity analysis for linear programming models. We do so by looking at the mathematical tools AIMMS provides to perform such an analysis. A sensitivity analysis can help you study how small changes in your initial data impact the optimal solution output. This type of sensitivity analysis is also called a post-optimality analysis. The webinar covers: 1. An introduction to sensitivity analysis 2. How to extract and interpret shadow prices and reduced cost in AIMMS 3. How to extract and interpret right hand side ranges and coefficient ranges in AIMMS
Views: 998 AIMMSChannel

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Get a Free Trial: https://goo.gl/C2Y9A5 Get Pricing Info: https://goo.gl/kDvGHt Ready to Buy: https://goo.gl/vsIeA5 Learn the desired frequency domain shapes for sensitivity and complementary sensitivity transfer functions in this MATLAB® Tech Talk by Carlos Osorio. Watch other MATLAB Tech Talk videos here: http://www.mathworks.com/videos/tech-talks/controls/index.html
Views: 24366 MATLAB

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This video shows how you can use MOEAFramework to run Sobol sensitivity analysis, which is a valuable diagnostic tool for scientific models. For more information, visit http://waterprogramming.wordpress.com.
Views: 1026 PatReedResearch

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Basic Sensitivity Analysis Calc1
Views: 33 Carroll Math

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MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses the Monte Carlo simulation, Roulette License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 417263 MIT OpenCourseWare

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Views: 155 GOAL PROJECT

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Sensitivity Analysis

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Views: 91490 Brian Douglas

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This is a example of using design sensitivity in Abaqus (DSA). Design sensitivity used to predict the sensitivity of some output variable, for example normal stress respect to changing design parameter, for example thickness of a shell or material properties.
Views: 325 ABAQUS Tutorial

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This short video showcases how to conduct inverse modeling (i.e. parameter estimation and sensitivity analysis) using HYDROSCAPE (The interface used in this video is an older version of HYDROSCAPE, but should work very similarly with newer interfaces) HYDROSCAPE and this video is protected under copyright law.
Views: 90 HYDROSCAPE

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Produced for BST 230 at the University of Kentucky for educational purposes. BMJ Article: http://dx.doi.org/10.1136/bmj.327.7417.716

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HOMER is the global standard in microgrid software, based on decades of listening to the needs of users around the world with experience in designing and deploying microgrids and distributed power systems that can include a combination of renewable power sources, storage, and fossil-based generation (either through a local generator or a power grid). This video covers the sensitivity analysis feature of a simulation, which is critical in understanding the robustness of a design.
Views: 4727 HOMER Energy

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Views: 1133 Build Sci

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COURSE LINK https://www.chemicalengineeringguy.com/courses/aspen-plus-intermediate-course/ Description The INTERMEDIATE Aspen Plus Course will show you how to model and simulate more complex Processes Analysis of Unit Operation will help you in order to simulate more complex chemical processes, as well as to analyse and optimize existing ones. You will learn about: - Better Flowsheet manipulation - Hierarchy, Flowsheeting, Sub-flowsheet creation - Logical Operators / Manipulators - Understand Property Method Selection and its effects on simulation results - Study of more rigorous unit operations - Model Analysis Tools such as sensitivity and optimization - Reporting Relevant Results Plot relevant data for Heaters, Columns ,Reactors, Pumps - Temperature Profiles, Concentration Profile, Pump Curves, Heat Curves, etc… - Up to 3 Case Studies (in-depth analysis) - All theory is backed up by more than 30 Practical Workshops! At the end of the course you will be able to setup more complex processes, increase your simulation and flow sheeting techniques, run it and debugging, get relevant results and make a deeper analysis of the process for further optimization. ---- Please show the love! LIKE, SHARE and SUBSCRIBE! More likes, sharings, suscribers: MORE VIDEOS! ----- CONTACT ME [email protected] www.ChemicalEngineeringGuy.com http://facebook.com/Chemical.Engineering.Guy You speak spanish? Visit my spanish channel -www.youtube.com/ChemEngIQA

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Monte Carlo Simulation Class Lecture Powerpoint https://drive.google.com/open?id=0Bz9Gf6y-6XtTV3lXM0dlUDA0MjQ Implement Monte Carlos Simuation in Microsoft Excel Analysis Guide https://drive.google.com/open?id=0Bz9Gf6y-6XtTZ2h5eG40eTFSdVU Source Data for the Practice https://drive.google.com/open?id=0Bz9Gf6y-6XtTUEJmRFp0LWRGdE0
Views: 66477 The Data Science Show

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OptiY® is an open and multidisciplinary design environment providing most modern optimization strategies and state of the art probabilistic algorithms for uncertainty, reliability, robustness, sensitivity analysis, fatigue life prediction, data-mining and meta-modeling. The simulation model can be considered as black box with inputs and outputs. Within, it is an open platform for different kind of model classes. The adaptation to a special simulation environment takes place by a suitable interface. Collaborating different simulation systems is possible as networks, finite-element-method, rigid body dynamics, also material test bench as control optimization for drives.
Views: 550 OptiY

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