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Mod-01 Lec-03 Lecture-03-Mathematical Modeling (Contd...1)
 
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Process Control and Instrumentation by Prof.A.K.Jana,prof.D.Sarkar Department of Chemical Engineering,IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 96371 nptelhrd
What is Math Modeling? Video Series Part 1: What is Math Modeling?
 
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Mathematical modeling provides answers to real world questions like “Which recycling program is best for my city?” “How will a flu outbreak affect the US,” or “Which roller coaster is the most thrilling?” In math modeling, you’ll use math to represent, analyze, make predictions or otherwise provide insight into real world phenomena. This is the first episode in this new math modeling video series and introduces the modeling process, setting the stage for the next six videos which dive into the specific steps to modeling.
Mathematical Modelling - Continuum Models - Systems and Conservation Principle (part 1/5)
 
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Professor Timo Tiihonen from the University of Jyväskylä gives a lecture on continuum models.
Views: 24 TTY Matematiikka
A Framework for Teaching Mathematical Modelling
 
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Part of a talk given by Dr Ang Keng Cheng (National Institute of Education, Singapore) at a Mathematical Modelling Seminar in January 2013. The speaker introduces a framework that serves as a guide for teachers to plan and prepare lessons in mathematical modelling involving real life problems. See: Ang, K.C., Mathematical Modelling in Singapore Schools: A Framework for Instruction. In Niss, M. (Ed), Mathematical Modelling - From Theory to Practice , 2015, (pp 57-72), World Scientific, Singapore.
Views: 400 KC Ang
1.1.3-Introduction: Mathematical Modeling
 
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These videos were created to accompany a university course, Numerical Methods for Engineers, taught Spring 2013. The text used in the course was "Numerical Methods for Engineers, 6th ed." by Steven Chapra and Raymond Canale.
Views: 96753 Jacob Bishop
System Dynamics and Control: Module 4 - Modeling Mechanical Systems
 
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Introduction to modeling mechanical systems from first principles. In particular, systems with inertia, stiffness, and damping are modeled by applying Newton's 2nd Law. Translational and rotational systems are discussed.
Views: 136260 Rick Hill
GCI2016: Mini-course 3: Basic Mathematical Models... - Lecture 1: Jacek Banasiak
 
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Mini-course 3: Basic Mathematical Models in Epidemiology and Species Invasion Jacek Banasiak, University of Pretoria General description: The workshop brings together participants with different backgrounds and levels of exposure to mathematical modelling. This mini-course aims at introducing notions that occur in modelling processes in life science such as discrete and continuous dynamical systems, modelling interactions, stability and long term behaviour of evolutionary processes. These concepts will be illustrated on compartmental models of mathematical epidemiology. We shall also discuss species and disease invasion modelled by reaction diffusion equations and their travelling wave solutions. Lecture 1: Principles of mathematical modelling a. Discrete versus continuous time, autonomous versus non-autonomous, linear versus nonlinear b. Modelling intra and inter-species interactions: mass action, Holling, etc c. Basic single species models in discrete and continuous time d. Concepts of stability in dynamical systems Lecture 2: Common epidemiological models a. Principles of construction of compartmental models: SI, SIR, SIS etc b. Vector-borne diseases: malaria model c. Basic questions and methods of analysis d. Some pitfalls in modelling Lecture 3: Invasion modelling a. Population models with space structure b. Travelling waves c. Examples.
Atmosphere chemistry: mathematical modelling - 1 (Guy Brasseur)
 
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Mathematical models are key tools that are used both to advance our understanding of atmospheric physical and chemical processes and to provide forecast information, just like for meteorology. What are the underlying principles? How do they work? What are their limitations? These are among the questions covered. This movie is the first part of the series of two talks. This lecture was given in the context of the Summer School organised in June 2013 by MACC-II, the European project in charge of developing and providing the range of services of the Copernicus programme on the composition of the Earth's atmosphere. More on MACC-II (http://atmosphere.copernicus.eu). More on Copernicus (http://copernicus.eu).
Views: 1783 Atmosphere Copernicus
Understanding PID Control, Part 5: Three Ways to Build a Model
 
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Tuning a PID controller requires that you have a representation of the system you’re trying to control. - Download Code Examples to Learn How to Automatically Tune PID Controller Gainshttps: http://bit.ly/2HKBh12 This could be the physical hardware or a mathematical representation of that hardware. If you have physical hardware, you could guess at some PID gains, run a test to see how it performs, and then tweak the gains as necessary. This guess-and-check brute force tuning method might work, but you have other, more precise, options available if you have a mathematical model of the system. Therefore, this video presents three different ways to model your system so that you can take advantage of each of these methods when tuning your controller. The first method uses a detailed understanding of the system to develop the model with first principles. The second method uses system identification, the known input, and resulting output signal to fit the data to the model structure of your choice. The third method creates a model by linearizing an existing nonlinear model around a given operating point. With any of these models, you can start the process of developing and tuning a PID controller. Watch more MATLAB Tech Talks: http://bit.ly/2rTc8Yp Check out more control system lectures on Brian's Channel: http://bit.ly/2IUlvkw Other References: Modeling a DC Motor in Simscape: http://bit.ly/2KrgDVw Modeling a DC Motor in Simulink and Simscape – Tutorial: http://bit.ly/2KFp8va Videos on System Identification: http://bit.ly/2KIM3Wu Linear Analysis Tool - Documentation: http://bit.ly/2KqEowO Determining Model Order and Input Delay - Example: http://bit.ly/2KGsXR1 Determining an Optimal Model Order Using Akaike’s Information Criterion – Example: http://bit.ly/2KyYEMb Get a free product Trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe © 2018 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names maybe trademarks or registered trademarks of their respective holders.
Views: 25550 MATLAB
Tony Lawson - confronting mathematical modelling in economics // Bloomsbury Confrontations
 
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Tony Lawson, reader in economics at the University of Cambridge, argues that the 90%+ of economics taught in the Western world that is based on mathematical modelling is useless. He argues that it not only fails to provide insight into social reality, it obstructs other attempts to provide insight. This presentation was given as part of a seminar entitled 'Confronting mathematical modelling in economics', which took place on 26th March 2014. This seminar was part of the Bloomsbury Confrontations seminar series organised by Better Economics UCLU. More info is available here: http://bettereconomicsuclu.tumblr.com/ Many thanks for Kaiying Yang for producing this video.
Views: 13910 Better Economics UCLU
Mathematical Modeling
 
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Our latest "Education Today" segment on NBC4 features math teacher Rachael Gorsuch relating how she helps upper school students understand how math principles can be applied outside of the classroom.
Views: 654 Columbus Academy
What is a Logistic Regression Model? The Mathematical Principle
 
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1. Data set for the first exercise can be downloaded here: https://drive.google.com/open?id=0Bz9Gf6y-6XtTVXdTbC10V0FuM0k 2. Data set (Oscars.xlsx) for the second exercise can be downloaded here: https://drive.google.com/open?id=0Bz9Gf6y-6XtTUEtBVy04Tl9vcjA 3. How to Implement Logistic Regression Analysis in R: https://www.youtube.com/watch?v=eScK5w5JcHI&list=PL_iP0SGUzx9SkFjSjPXXGmyiMxxpLRC36
Views: 1561 The Data Science Show
Problem Solving and Mathematical Modelling (Part 3)
 
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Keynote lecture given by Dr Ang Keng Cheng at the Mathematics Teachers Conference (MTC) jointly organized by the Mathematics and Mathematics Education (MME) academic group of the NIE, Singapore, and the Association of Mathematics Educators (AME) in 2008.
Views: 7682 KC Ang
CONTROL SYSTEMS (ELECTRICAL ENGINEERING) : Mathematical Modeling
 
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Website: www.gateee.com Facebook Page: https://www.facebook.com/gate4ee
Views: 939 GATE EE
Modeling Physical Components, Part 1: Mathematical Models - MATLAB and Simulink Racing Lounge
 
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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 This is the first video of a two-part episode. Model the physical systems of your racecar and learn about the variety of modeling methods that fit your needs. This is the first video of a two-part episode. Sebastian Castro and Christoph Hahn, of MathWorks, introduce a variety of modeling approaches and demonstrate them in Simulink®. Sebastian describes the differences between plant models and algorithmic models and how to decide which model is best for your situation. Plant models are a virtual prototype of a real physical system that you are trying to model, whereas an algorithmic model is where you can test your algorithms on your simulation. With Simulink you can combine these two types of modeling and use available analysis tools to then further improve your design. The many varieties of modeling include those using purely data or purely mathematical equations, and then combinations using both of these methods. Sebastian starts by looking at a suspension system using the mathematical modeling method to see how easy-to-understand equations can be used to create a model. With that same suspension system, he then moves the focus away from equations and demonstrates component-based modeling using Simscape™. This is a tool for component-based modeling that uses blocks which have the equations in the background. The benefit of this method is that you do not need to know as much detail about the equations.
Views: 53034 MATLAB
Mathematical Models of the Ebola Epidemic in West Africa: Principles, Predictions, and Control
 
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The Ebola epidemic in West Africa has spurred an international response. This response has been strongly influenced by epidemiological models that predicted a devastating rise in cases without large-scale changes in behavior and intervention. In this talk, I introduce the mathematical principles underlying predictions of the rate and scope of a disease epidemic. I then explain how such principles have been applied to forecasting Ebola virus disease (EVD) dynamics and identifying the type and scale of necessary control. One control mechanism involves influencing behavior and social norms to limit post-death transmission, e.g., during burial ceremonies of individuals who died from EVD. Post-death transmission for EVD has been recognized for over 10 years, yet its relative importance in the current epidemic remains uncertain. I conclude my talk with an analysis of ongoing challenges in estimating the relative importance of post-death transmission from early-stage epidemic data. I show why such estimation is hard and yet, nonetheless, why controlling post-death transmission is likely to have a substantial effect on short- and long-term epidemic outcomes.
Views: 424 School of Biology
IMA Public Lectures:Mathematical modeling in medicine,sports, and the environment; Alfio Quarteroni
 
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Institute for Mathematics and its Applications (IMA) Public Lecture Series http://www.ima.umn.edu/public-lecture/ Mathematical modeling in medicine, sports, and the environment 7:00P.M., February 13, 2008, Willey Hall 125 Alfio Quarteroni (École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland and Politecnico di Milano, Milan, Italy) Mathematical models are enabling advances in increasingly complex areas of engineering and technology. Recent develoP.M.ents in multiscale geometrical modeling have opened the way to progress in modeling such complex systems as the human circulatory system and the climate system. Professor Quarteroni leads a team which has harnessed mathematical modeling to design improved cardiac surgical interventions and to optimize the design of the twice winning America's cup yacht Alinghi. He will talk about this work, and their efforts to confront some of the great environmental challenges that face us.
Views: 1193 IMA UMN
Atmosphere chemistry: mathematical modelling - 2 (Guy Brasseur)
 
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Mathematical models are key tools that are used both to advance our understanding of atmospheric physical and chemical processes and to provide forecast information, just like for meteorology. What are the underlying principles? How do they work? What are their limitations? These are among the questions covered. This movie is the second part of the series of two talks. This lecture was given in the context of the Summer School organised in June 2013 by MACC-II, the European project in charge of developing and providing the range of services of the Copernicus programme on the composition of the Earth's atmosphere. More on MACC-II (http://atmosphere.copernicus.eu). More on Copernicus (http://copernicus.eu).
Mathematical Planning of Orthopaedic Surgery
 
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The mathematical modelling and simulation of human motion in the context of computer assisted surgery is a challenging task. For example, the design of fixation devices in surgery and joint prostheses requires a realistic modelling of the healing process and a quantitative description of displacements and stresses during different gaits, e.g., walking and stair climbing. A realistic physical and mathematical modelling in combination with efficient simulation techniques would open exciting medical perspectives far beyond osteotomic surgery. Mathematical models coupling all possibly relevant biomechanical aspects of human motion are clearly not manageable. Existing musculo-skeletal models for human motion are typically based on a rigid body approximation of human bones leading to multibody systems of differential-algebraic equations (DAEs). Joints and muscles are incorporated by simple models. To study the local elastic behavior of bones, finite element methods (FEM) are used for 3D partial differential equations (PDEs). In applications such as prostheses or fixation devices, mechanical contact problems occur. Quite often, contact problems are just neglected for computational complexity reasons. If at all, they are usually treated via penalty methods or heuristic techniques. Heterogeneous dynamical DAE/PDE models, which would actually be required, seem to be not available yet.
Views: 2393 MatheonBerlin
Mathematics of Epidemics | Trish Campbell | TEDxYouth@Frankston
 
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Using the example of how videos and images can become viral on the internet Trish Campbell explores the role that mathematical modeling plays in understanding the spread of infectious diseases. Trish Campbell is a postdoctoral researcher at the Melbourne School of Population and Global Health and an Honorary Fellow at the Murdoch Childrens Research Institute. Trish’s PhD research improved understanding of how whooping cough spreads and developed strategies to protect infants too young to be vaccinated. Continuing in the field of infectious diseases, Trish has recently started researching the spread of skin infections, a major cause of poor health in Australian Indigenous populations. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 1379 TEDx Talks
Principles of Modeling for Cyber-Physical Systems - Course Introduction
 
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Fall 2018 graduate level course on Principles of Modeling for Cyber-Physical Systems. Weekly lectures will be posted on the course website (https://linklab-uva.github.io/modeling_cps/) and on this channel starting September 3rd 2018. Course Instructor: Prof Madhur Behl Computer Science | Systems and Information Engineering University of Virginia
Views: 516 Madhur Behl
GCI2016: Mini-course 3: Basic Mathematical Models... - Lecture 2: Jacek Banasiak
 
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Mini-course 3: Basic Mathematical Models in Epidemiology and Species Invasion Jacek Banasiak, University of Pretoria General description: The workshop brings together participants with different backgrounds and levels of exposure to mathematical modelling. This mini-course aims at introducing notions that occur in modelling processes in life science such as discrete and continuous dynamical systems, modelling interactions, stability and long term behaviour of evolutionary processes. These concepts will be illustrated on compartmental models of mathematical epidemiology. We shall also discuss species and disease invasion modelled by reaction diffusion equations and their travelling wave solutions. Lecture 1: Principles of mathematical modelling a. Discrete versus continuous time, autonomous versus non-autonomous, linear versus nonlinear b. Modelling intra and inter-species interactions: mass action, Holling, etc c. Basic single species models in discrete and continuous time d. Concepts of stability in dynamical systems Lecture 2: Common epidemiological models a. Principles of construction of compartmental models: SI, SIR, SIS etc b. Vector-borne diseases: malaria model c. Basic questions and methods of analysis d. Some pitfalls in modelling Lecture 3: Invasion modelling a. Population models with space structure b. Travelling waves c. Examples.
GCI2016: Mini-course 3: Basic Mathematical Models... - Lecture 3: Jacek Banasiak
 
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Mini-course 3: Basic Mathematical Models in Epidemiology and Species Invasion Jacek Banasiak, University of Pretoria General description: The workshop brings together participants with different backgrounds and levels of exposure to mathematical modelling. This mini-course aims at introducing notions that occur in modelling processes in life science such as discrete and continuous dynamical systems, modelling interactions, stability and long term behaviour of evolutionary processes. These concepts will be illustrated on compartmental models of mathematical epidemiology. We shall also discuss species and disease invasion modelled by reaction diffusion equations and their travelling wave solutions. Lecture 1: Principles of mathematical modelling a. Discrete versus continuous time, autonomous versus non-autonomous, linear versus nonlinear b. Modelling intra and inter-species interactions: mass action, Holling, etc c. Basic single species models in discrete and continuous time d. Concepts of stability in dynamical systems Lecture 2: Common epidemiological models a. Principles of construction of compartmental models: SI, SIR, SIS etc b. Vector-borne diseases: malaria model c. Basic questions and methods of analysis d. Some pitfalls in modelling Lecture 3: Invasion modelling a. Population models with space structure b. Travelling waves c. Examples.
Mathematical Modeling and Simulation of Nematic Liquid Crystals (A Montage)
 
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This music video illustrates applied mathematics for the simulation of liquid crystal phenomena. A brief intro on this can be found here: http://www.math.lsu.edu/~walker/liquid_crystal.html Special thanks to Duff Paulsen for granting permission to use his music as the soundtrack. The track "Undertow" can be found here: http://www.amazon.com/Somewhere-In-California-The-Torquays/dp/B000A6VY3G
Views: 2325 Shawn Walker
Lotka Volterra model | competition model and predator prey model with equation
 
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Lotka Volterra predator prey model - In this lecture lotka voltera competition model is explained with equation. This is a two part video. in the first part, Lotka Voltera competition model is explained and in the second part Lotka Voltera predator prey model is exaplained with equation. INTERSPECIFIC COMPETITION: LOTKA-VOLTERRA Introduction: Interspecific competition refers to the competition between two or more species for some limiting resource. This limiting resource can be food or nutrients, space, mates, nesting sites-- anything for which demand is greater than supply. When one species is a better competitor, interspecific competition negatively influences the other species by reducing population sizes and/or growth rates, which in turn affects the population dynamics of the competitor. The Lotka-Volterra model of interspecific competition is a simple mathematical model that can be used to understand how different factors affect the outcomes of competitive interactions. Importance: Competitive interactions between organisms can have a great deal of influence on species evolution, the structuring of communities (which species coexist, which don't, relative abundances, etc.), and the distributions of species (where they occur). Modeling these interactions provides a useful framework for predicting outcomes. Article source: http://www.tiem.utk.edu/~gross/bioed/bealsmodules/competition.html For more information, log on to- http://www.shomusbiology.com/ Get Shomu's Biology DVD set here- http://www.shomusbiology.com/dvd-store/ Download the study materials here- http://shomusbiology.com/bio-materials.html Remember Shomu’s Biology is created to spread the knowledge of life science and biology by sharing all this free biology lectures video and animation presented by Suman Bhattacharjee in YouTube. All these tutorials are brought to you for free. Please subscribe to our channel so that we can grow together. You can check for any of the following services from Shomu’s Biology- Buy Shomu’s Biology lecture DVD set- www.shomusbiology.com/dvd-store Shomu’s Biology assignment services – www.shomusbiology.com/assignment -help Join Online coaching for CSIR NET exam – www.shomusbiology.com/net-coaching We are social. Find us on different sites here- Our Website – www.shomusbiology.com Facebook page- https://www.facebook.com/ShomusBiology/ Twitter - https://twitter.com/shomusbiology SlideShare- www.slideshare.net/shomusbiology Google plus- https://plus.google.com/113648584982732129198 LinkedIn - https://www.linkedin.com/in/suman-bhattacharjee-2a051661 Youtube- https://www.youtube.com/user/TheFunsuman Thank you for watching the ecology lecture on Lotka voltera model equation and the competition and predator prey model.
Views: 62375 Shomu's Biology
The Advantages of a Mathematical Model for Investing
 
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The Advantages of a Mathematical Model for Investing. Part of the series: Personal Finance Tips. When it comes to investing, a mathematical model certainly has its fair share of advantages. Learn about the advantages of a mathematical model for investing with help from the manager at an independent investment advisory firm in this free video clip. Read more: http://www.ehow.com/video_12199678_advantages-mathematical-model-investing.html
Views: 124 ehowfinance
Mathematical Model
 
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This video is from the “Truss” module in the course “A Hands-on Introduction to Engineering Simulations” from Cornell University at edx.org.
Views: 663 Cx Simulations
Mathematical Modeling: Material Balances
 
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Develops a mathematical model for a chemical process using material balances. Made by faculty at Lafayette College and produced by the University of Colorado Boulder, Department of Chemical & Biological Engineering. Check out our Process Control playlist: https://www.youtube.com/playlist?list=PL4xAk5aclnUhfXrHhv_ZQfbQ6w6ahwfFQ Are you using a textbook? Check out our website for screencasts organized by popular textbooks: http://www.learncheme.com/screencasts/process-controls Check out our website for interactive Process Control simulations: http://www.learncheme.com/simulations/process-control
Views: 17649 LearnChemE
Mathematical Models and Planning of Urban Infrastructure Networks
 
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Mathematical Models and Planning of Urban Infrastructure Networks - Sir Alan Wilson, Alan Turing Institute CEO This video was produced by the Isaac Newton Institute
Getting Started with Math Modeling
 
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Math comes in handy for answering questions about a variety of topics, from calculating the cost-effectiveness of fuel sources and determining the best regions to build high-speed rail to predicting the spread of disease and assessing roller coasters on the basis of their "thrill" factor. How does math do all that? That is the topic of this free handbook published by the Society for Industrial and Applied Mathematics (SIAM): "Math Modeling: Getting Started and Getting Solutions." PDFs of the book are available for free download at http://m3challenge.siam.org/about/mm/. Print copies are available upon request for $15 per copy to cover the cost of printing and mailing. Please contact SIAM Customer Service at +1-215-382-9800 or toll-free 800-447-SIAM (US and Canada) to order a print copy of the handbook.
Math 4. Math for Economists. Lecture 01. Introduction to the Course
 
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UCI Math 4: Math for Economists (Summer 2013) Lec 01. Math for Economists View the complete course: http://ocw.uci.edu/courses/math_4_math_for_economists.html Instructor: Jason Kronewetter, Ph.D. License: Creative Commons CC-BY-SA Terms of Use: http://ocw.uci.edu/info More courses at http://ocw.uci.edu Description: UCI Math 4 covers the following topics: linear algebra and multivariable differential calculus suitable for economic applications. Recorded on August 5, 2013 Required attribution: Kronewetter, Jason. Math for Economists 4 (UCI OpenCourseWare: University of California, Irvine), http://ocw.uci.edu/courses/math_4_math_for_economists.html. [Access date]. License: Creative Commons Attribution-ShareAlike 3.0 United States License. (http://creativecommons.org/licenses/by-sa/3.0/deed.en_US)
Views: 268015 UCI Open
QL presentation of Mathematical Modeling in every day life.
 
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Here is my groups presentation on Mathematical that can help you in your every day life.
Views: 35 CaptnHapHazard
GAN vs ODE: the end of mathematical modeling? - Alexandr Honchar, MAWI
 
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For hundreds of years, scientists were developing strong theories and rigorous mathematical models to explain patterns and dependencies in data and processes around us. Today instead of modeling some features of data by ourselves we rely on deep neural networks and they don't let us down. So, the natural question arises: do we really need human experts to describe the world mathematically or let's just let AI do all the work? In this talk, we will connect dots between generative neural networks (GANs) and mathematical models like ODEs (ordinary differential equations) for ECG analysis: a classical area driven by pure mathematical models for decades. We analyze empirically what human experts did and what neural networks have learned by themselves and will try to understand, how close we are to fully rely on AI in ECG analysis and other areas.
Views: 163 Data Science UA
Using Mathematical Models to Help Understand Planar Cell Pol
 
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March 7, 2007 In this talk, methods that we have designed to analyze and help to identify certain protein regulatory networks will be presented. Hybrid automata represent one suitable modeling framework, as the protein concentration dynamics inside each cell are modeled using linear differential equations; inputs activate or deactivate these continuous dynamics through discrete switches, which themselves are controlled by protein concentrations reaching given thresholds. We present an iterative refinement algorithm for computing discrete abstractions of a class of symbolic hybrid automata, and we apply this algorithm to a model of multiple cell Delta-Notch protein signaling. The results are analyzed to show that novel, non-intuitive, and biologically interesting properties can be deduced from the computation, thus demonstrating that mathematical modeling which extrapolates from existing information and underlying principles can be successful in increasing understanding of some biological systems.
Views: 3553 CITRIS
Proof by Mathematical Induction - How to do a Mathematical Induction Proof ( Example 1 )
 
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In this tutorial I show how to do a proof by mathematical induction. Learn Math Tutorials Bookstore http://amzn.to/1HdY8vm Donate http://bit.ly/19AHMvX STILL NEED MORE HELP? Connect one-on-one with a Math Tutor. Click the link below: https://trk.justanswer.com/aff_c?offer_id=2&aff_id=8012&url_id=232 :)
Views: 712389 Learn Math Tutorials
Lecture - 30 Mathematical Models for Facility Location
 
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Lecture series on Project and Production Management by Prof. Arun kanda, Department of Mechanical Engineering, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 19091 nptelhrd
Mod-01 Lec-05 Lectur-05-Mathematical Modeling (Contd...3)
 
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Process Control and Instrumentation by Prof.A.K.Jana,prof.D.Sarkar Department of Chemical Engineering,IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 21954 nptelhrd
Dr Sebastian Funk Forecasting an Ebola Epidemic using Mathematical models
 
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Talk from the London Branch of the Institute of Mathematics and its Applications, Wednesday 21 October 2015
Views: 448 IMAmaths
Mathematical modeling of one-dimensional oil displacement by (...) - Adolfo Pires
 
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Conservation Laws and Applications, celebrating the 70th birthday of Dan Marchesin Adolfo Pires - Mathematical modeling of one-dimensional oil displacement by combined solvent-thermal flooding Site: https://impa.br/eventos-do-impa/eventos-2017/conservation-laws-and-applications-celebrating-the-70th-birthday-of-dan-marchesin/ Download: http://video.impa.br/index.php?page=conservation-laws-and-applications-celebrating-the-70th-birthday-of-dan-marchesin Organizing Committee Alexei A. Mailybaev (IMPA) Amaury Alvarez Cruz (IMPA) Scientific Committee Johannes Bruining (TU Delft, The Netherlands) Alexei A. Mailybaev (IMPA, Brazil) Aparecido J. de Souza (UFPB, Brazil) Introduction The Workshop is focused on fundamental and applied topics in the theory of hyperbolic conservation laws. It is aimed for a discussion of questions that range from pure mathematical issues (existence, uniqueness and structure of solutions) to mathematical modeling and numerical analysis in fluid dynamics and problems of porous media. Contact Postal Address: Instituto Nacional de Matemática Pura e Aplicada Estrada Dona Castorina 110, Jardim Botânico Rio de Janeiro, RJ, CEP 22460-320, Brasil E-mails: [email protected] [email protected] IMPA - Instituto Nacional de Matemática Pura e Aplicada © http://www.impa.br | http://video.impa.br
15. Introduction to Lagrange With Examples
 
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MIT 2.003SC Engineering Dynamics, Fall 2011 View the complete course: http://ocw.mit.edu/2-003SCF11 Instructor: J. Kim Vandiver License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
Views: 326513 MIT OpenCourseWare
Post-Turing tissue pattern formation: Insights from mathematical modelling, Anna Marciniak-Czochra
 
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Post-Turing tissue pattern formation: Insights from mathematical modelling, Anna Marciniak-Czochra, Heidelberg University Cells and tissue are objects of the physical world, and therefore they obey the laws of physics and chemistry, notwithstanding the molecular complexity of biological systems. What are the mathematical principles that are at play in generating such complex entities from simple laws? Understanding the role of mechanical and mechano-chemical interactions in cell processes, tissue development, regeneration and disease has become a rapidly expanding research field in the life sciences. To reveal the patterning potential of mechano-chemical interactions, we have developed two classes of mathematical models coupling dynamics of diffusing molecular signals with a model of tissue deformation. First, we derived a model based on energy minimization that leads to 4-th order partial differential equations of evolution of infinitely thin deforming tissue (pseudo-3D model) coupled with a surface reaction-diffusion equation. The second approach (full-3D model) consists of a continuous model of large tissue deformation coupled with a discrete description of spatial distribution of cells to account for active deformation of single cells. The models account for a range of mechano-chemical feedbacks, such as signalling-dependent strain, stress, or tissue compression. Numerical simulations based on the arbitrary Lagrangian-Eulerian and fully Eulerian formulations show ability of the proposed mechanisms to generate development of various spatio-temporal structures. In this study, we compare the resulting patterns of tissue invagination and evagination to those encountered in developmental biology. The new class of patterns is compared to the classical Turing patterns. Analytical and numerical challenges of the proposed models are discussed.
Principle of Mathematical Induction 1
 
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Intro to the Principle of Mathematical Induction
Views: 135307 AbsoluteMathematics
Principles, Applications of mathematical Induction || Divisible model 2 || Disk Telangana
 
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