Thanks for Contributing! You just created a new WN page. Learn more »
Sketching Solutions of 2x2 Homogeneous Linear System with Constant Coefficients View the complete course: http://ocw.mit.edu/18-03S06 License: Creative Commo...
Mathematics of Complexity lecture 3 Class description: We've all heard the buzzwords - chaos, fractals, networks, power laws. What do these terms mean in a r...
Discussion of differential equations as a representation of dynamic systems. Introduction to the Laplace Transform as a tool for solving differential equations.
Enroll in Introduction to Differential Equations from BUx at: https://www.edx.org/course/introduction-differential-equations-bux-math226-1x Learn the mathematical theory of ordinary differential equations and its application to biological and physical systems. About this Course Phenomena as diverse as the motion of the planets, the spread of a disease, and the oscillations of a suspension bridge are governed by differential equations. MATH226x is an introduction to the mathematical theory of ordinary differential equations. This course follows a modern dynamical systems approach to the subject. In particular, equations are analyzed using qualitative, numerical, and if possible, symbolic techniques. MATH226x is essentially the edX equivalent of MA226, a one-semester course in ordinary differential equations taken by more than 500 students per year at Boston University. It is divided into three parts. MATH226.1x is the first of these three parts. In MATH226.1x, we will discuss biological and physical models that can be expressed as differential equations with one or two dependent variables. We will discuss geometric/qualitative and numerical techniques that apply to all differential equations. When possible, we will study some of the standard symbolic solution techniques such as separation of variables and the use of integrating factors. We will also study the theory of existence and uniqueness of solutions, the phase line and bifurcations for first-order autonomous systems, and the phase plane for two-dimensional autonomous systems. The techniques that we develop will be used to analyze models throughout the course.
State Space Representation Dynamic Systems.
Nonlinear Dynamical Systems by Prof. Harish K. Pillai and Prof. Madhu N.Belur,Department of Electrical Engineering,IIT Bombay.For more details on NPTEL visit http://nptel.ac.in
I will show how to model multiagent systems using dynamical systems theory by deriving a class of macroscopic differential equations that describe mutual ada...
Developing a dynamic simulation model from first principles is accomplished by expressing the physical system by differential equations. This tutorial demons...
What is Dynamical systems theory? A report all about Dynamical systems theory for homework/assignment Dynamical systems theory is an area of mathematics used to describe the behavior of complex dynamical systems, usually by employing differential equations or difference equations. When differential equations are employed, the theory is called continuous dynamical systems. When difference equations are employed, the theory is called discrete dynamical systems. When the time variable runs over a set that is discrete over some intervals and continuous over other intervals or is any arbitrary time-set such as a cantor set—one gets dynamic equations on time scales. Some situations may also be modeled by mixed operators, such as differential-difference equations. Intro/Outro music: Discovery Hit/Chucky the Construction Worker - Kevin MacLeod (incompetech.com) Licensed under CC-BY-3.0 Text derived from: http://en.wikipedia.org/wiki/Dynamical_systems_theory Text to Speech powered by voice-rss.com Images are Public Domain or CC-BY-3.0: 2048px-Lorenz_attractor_yb.svg.png from http://en.wikipedia.org/wiki/Dynamical_systems_theory 240px-Lorenz_attractor_yb.svg.png from http://simple.wikipedia.org/wiki/Dynamical_systems_theory 2000px-Lorenz_attractor_yb.svg.png from http://zh.wikipedia.org/wiki/%E5%8A%A8%E6%80%81%E7%B3%BB%E7%BB%9F%E7%90%86%E8%AE%BA 150px-Complex_systems_organizational_map.jpg from http://en.wikipedia.org/wiki/Complex_systems
In this lecture, we go through the steps of linearizing non-linear differential equations about a given operating/equilibrium point. We will use the multi-va...
In mathematics, a partial differential equation (PDE) is a differential equation that contains unknown multivariable functions and their partial derivatives....
Solving first order, linear, time-varying differential equations using integrating factors is explored. After a brief illustration of how an integrating factor is helpful, an example is worked where the integrating factor is assumed known. The process of deriving the integrating factor is next, followed by a revisiting of the example. The presentation is general, but the eventual motivation (in future videos) is for exploring linearity of dynamical systems control system analysis / design.
A differential equation is a mathematical equation that relates some function of one or more variables with its derivatives. Differential equations arise whenever a deterministic relation involving some continuously varying quantities (modeled by functions) and their rates of change in space and/or time (expressed as derivatives) is known or postulated. Because such relations are extremely common, differential equations play a prominent role in many disciplines including engineering, physics, economics, and biology. Differential equations are mathematically studied from several different perspectives, mostly concerned with their solutions — the set of functions that satisfy the equation. Only the simplest differential equations are solvable by explicit formulas; however, some properties of solutions of a given differential equation may be determined without finding their exact form. If a self-contained formula for the solution is not available, the solution may be numerically approximated using computers. The theory of dynamical systems puts emphasis on qualitative analysis of systems described by differential equations, while many numerical methods have been developed to determine solutions with a given degree of accuracy. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
The first in a series of lectures which will examine differential equations from the perspective of the exponential map. The first lecture starts by consider...
The seventh in a series of lectures which will examine differential equations from the perspective of the exponential map. The seventh lecture continues with...
The eleventh in a series of lectures which will examine differential equations from the perspective of the exponential map. In the eleventh lecture we comput...
Interactive Exploration of a Dynamical System Follow us on Twitter - https://twitter.com/ExcellentVideos Facebook - http://www.facebook.com/excellent.randomv...
RI Seminar, February 18, 2011 Andre Platzer Assistant Professor, Computer Science Department, Carnegie Mellon University Hybrid systems model cyber-physical ...
I hope you found this video useful, please subscribe for daily videos! WBM Foundations: Mathematical logic Set theory Algebra: Number theory Group theory Lie...
As I detailed at the beginning of the video, this was originally for personal use, though I decided if one person finds this useful, it is worth uploading. I hope you found this video useful, please subscribe for daily videos! WBM Foundations: Mathematical logic Set theory Algebra: Number theory Group theory Lie groups Commutative rings Associative ring theory Nonassociative ring theory Field theory General algebraic systems Algebraic geometry Linear algebra Category theory K-theory Combinatorics and Discrete Mathematics Ordered sets Geometry Geometry Convex and discrete geometry Differential geometry General topology Algebraic topology Manifolds Analysis Calculus and Real Analysis: Real functions Measure theory and integration Special functions Finite differences and functional equations Sequences and series Complex analysis Complex variables Potential theory Multiple complex variables Differential and integral equations Ordinary differential equations Partial differential equations Dynamical systems Integral equations Calculus of variations and optimization Global analysis, analysis on manifolds Functional analysis Functional analysis Fourier analysis Abstract harmonic analysis Integral transforms Operator theory Numerical analysis and optimization Numerical analysis Approximations and expansions Operations research Probability and statistics Probability theory Statistics Computer Science Computer science Information and communication Applied mathematics Mechanics of particles and systems Mechanics of solids Fluid mechanics Optics, electromagnetic theory Classical thermodynamics, heat transfer Quantum Theory Statistical mechanics, structure of matter Relativity and gravitational theory Astronomy and astrophysics Geophysics applications Systems theory Other sciences Category
I hope you found this video useful, please subscribe for daily videos! WBM Foundations: Mathematical logic Set theory Algebra: Number theory Group theory Lie groups Commutative rings Associative ring theory Nonassociative ring theory Field theory General algebraic systems Algebraic geometry Linear algebra Category theory K-theory Combinatorics and Discrete Mathematics Ordered sets Geometry Geometry Convex and discrete geometry Differential geometry General topology Algebraic topology Manifolds Analysis Calculus and Real Analysis: Real functions Measure theory and integration Special functions Finite differences and functional equations Sequences and series Complex analysis Complex variables Potential theory Multiple complex variables Differential and integral equations Ordinary differential equations Partial differential equations Dynamical systems Integral equations Calculus of variations and optimization Global analysis, analysis on manifolds Functional analysis Functional analysis Fourier analysis Abstract harmonic analysis Integral transforms Operator theory Numerical analysis and optimization Numerical analysis Approximations and expansions Operations research Probability and statistics Probability theory Statistics Computer Science Computer science Information and communication Applied mathematics Mechanics of particles and systems Mechanics of solids Fluid mechanics Optics, electromagnetic theory Classical thermodynamics, heat transfer Quantum Theory Statistical mechanics, structure of matter Relativity and gravitational theory Astronomy and astrophysics Geophysics applications Systems theory Other sciences Category
At the Adachi Laboratory, the theme of research is modeling and control. Control means actively changing a systems dynamics in a desired way. Control is util...
Professor Ugur Abdulla, Florida Institute of Technology View in HD on the FIT Site: http://media.fit.edu/load.php?fn=Math%20Seminar%20-%20Part%201-%20Dr.%20U...
I hope you found this video useful, please subscribe for daily videos! WBM Foundations: Mathematical logic Set theory Algebra: Number theory Group theory Lie groups Commutative rings Associative ring theory Nonassociative ring theory Field theory General algebraic systems Algebraic geometry Linear algebra Category theory K-theory Combinatorics and Discrete Mathematics Ordered sets Geometry Geometry Convex and discrete geometry Differential geometry General topology Algebraic topology Manifolds Analysis Calculus and Real Analysis: Real functions Measure theory and integration Special functions Finite differences and functional equations Sequences and series Complex analysis Complex variables Potential theory Multiple complex variables Differential and integral equations Ordinary differential equations Partial differential equations Dynamical systems Integral equations Calculus of variations and optimization Global analysis, analysis on manifolds Functional analysis Functional analysis Fourier analysis Abstract harmonic analysis Integral transforms Operator theory Numerical analysis and optimization Numerical analysis Approximations and expansions Operations research Probability and statistics Probability theory Statistics Computer Science Computer science Information and communication Applied mathematics Mechanics of particles and systems Mechanics of solids Fluid mechanics Optics, electromagnetic theory Classical thermodynamics, heat transfer Quantum Theory Statistical mechanics, structure of matter Relativity and gravitational theory Astronomy and astrophysics Geophysics applications Systems theory Other sciences Category
This mini lecture examines the mathematical commonality between different types of systems - mechanical, electrical and thermal.
PROGRAM: RECENT TRENDS IN ERGODIC THEORY AND DYNAMICAL SYSTEMS DATES: Tuesday 18 Dec, 2012 - Saturday 29 Dec, 2012 VENUE: Department of Mathematics,Faculty o...
The second in a series of lectures which will examine differential equations from the perspective of the exponential map. The second lecture starts by consid...
The third in a series of lectures which will examine differential equations from the perspective of the exponential map. The third lecture develops the compl...
The sixth in a series of lectures which will examine differential equations from the perspective of the exponential map. The sixth lecture introduces very ba...
The ninth in a series of lectures which will examine differential equations from the perspective of the exponential map. The ninth lecture discusses coordina...
The tenth in a series of lectures which will examine differential equations from the perspective of the exponential map. The tenth lecture discusses coordina...
By: Mariana Haragus, Université de Franche-Comté, Besançon, France - Date: 2010-07-15 12:00:00 - Description: Nonlinear waves are particular solutions of p...
I hope you found this video useful, please subscribe for daily videos! WBM Foundations: Mathematical logic Set theory Algebra: Number theory Group theory Lie groups Commutative rings Associative ring theory Nonassociative ring theory Field theory General algebraic systems Algebraic geometry Linear algebra Category theory K-theory Combinatorics and Discrete Mathematics Ordered sets Geometry Geometry Convex and discrete geometry Differential geometry General topology Algebraic topology Manifolds Analysis Calculus and Real Analysis: Real functions Measure theory and integration Special functions Finite differences and functional equations Sequences and series Complex analysis Complex variables Potential theory Multiple complex variables Differential and integral equations Ordinary differential equations Partial differential equations Dynamical systems Integral equations Calculus of variations and optimization Global analysis, analysis on manifolds Functional analysis Functional analysis Fourier analysis Abstract harmonic analysis Integral transforms Operator theory Numerical analysis and optimization Numerical analysis Approximations and expansions Operations research Probability and statistics Probability theory Statistics Computer Science Computer science Information and communication Applied mathematics Mechanics of particles and systems Mechanics of solids Fluid mechanics Optics, electromagnetic theory Classical thermodynamics, heat transfer Quantum Theory Statistical mechanics, structure of matter Relativity and gravitational theory Astronomy and astrophysics Geophysics applications Systems theory Other sciences Category
Equações Diferenciais Ordinárias Teorema de existência e unicidade. Dependência diferenciável das condições iniciais. Equações lineares. Exponencial de matrizes. Classificação dos campos lineares. Forma canônica de Jordan. Equações lineares não autônomas: solução fundamental e teorema de Liouville. Equações lineares não homogêneas. Equações com coeficientes periódicos, teorema de Floquet. Estabilidade e instabilidade assintótica de um ponto singular de uma equação autônoma. Funções de Lyapounov. Pontos fixos hiperbólicos. Enunciado do teorema de linearização de Grobman-Hartman. Fluxo associado a uma equação autônoma. Conjuntos limites. Campos gradientes. Campos Hamiltonianos. Campos no plano: órbitas periódicas e teorema de Poincaré-Bendixon. Órbitas periódicas hiperbólicas. Equação de Van der Pol. Referências: ARNOLD, V. - Equations Differentialles Ordinaires. Moscou, Ed. Mir, 1974. HIRSCH, M. e SMALE, S. - Differential Equations, Dynamical Systems and Linear Algebra. New York, Academic Press, 1974. PONTRYAGIN, L. S. - Ordinary Differential Equations. Reading, Mass., Addison-Wesley, 1969. SOTOMAYOR, J. - Lições de Equações Diferenciais Ordinárias. Rio de Janeiro, IMPA, Projeto Euclides, 1979. Marcelo Viana
Equações Diferenciais Ordinárias Teorema de existência e unicidade. Dependência diferenciável das condições iniciais. Equações lineares. Exponencial de matrizes. Classificação dos campos lineares. Forma canônica de Jordan. Equações lineares não autônomas: solução fundamental e teorema de Liouville. Equações lineares não homogêneas. Equações com coeficientes periódicos, teorema de Floquet. Estabilidade e instabilidade assintótica de um ponto singular de uma equação autônoma. Funções de Lyapounov. Pontos fixos hiperbólicos. Enunciado do teorema de linearização de Grobman-Hartman. Fluxo associado a uma equação autônoma. Conjuntos limites. Campos gradientes. Campos Hamiltonianos. Campos no plano: órbitas periódicas e teorema de Poincaré-Bendixon. Órbitas periódicas hiperbólicas. Equação de Van der Pol. Referências: ARNOLD, V. - Equations Differentialles Ordinaires. Moscou, Ed. Mir, 1974. HIRSCH, M. e SMALE, S. - Differential Equations, Dynamical Systems and Linear Algebra. New York, Academic Press, 1974. PONTRYAGIN, L. S. - Ordinary Differential Equations. Reading, Mass., Addison-Wesley, 1969. SOTOMAYOR, J. - Lições de Equações Diferenciais Ordinárias. Rio de Janeiro, IMPA, Projeto Euclides, 1979. Marcelo Viana
Professor Huaizhong Zhao from the Department of Mathematical Sciences gives his Inaugural Lecture for Loughborough University, 'Stochastic (Partial) Differen...
Equações Diferenciais Ordinárias Teorema de existência e unicidade. Dependência diferenciável das condições iniciais. Equações lineares. Exponencial de matrizes. Classificação dos campos lineares. Forma canônica de Jordan. Equações lineares não autônomas: solução fundamental e teorema de Liouville. Equações lineares não homogêneas. Equações com coeficientes periódicos, teorema de Floquet. Estabilidade e instabilidade assintótica de um ponto singular de uma equação autônoma. Funções de Lyapounov. Pontos fixos hiperbólicos. Enunciado do teorema de linearização de Grobman-Hartman. Fluxo associado a uma equação autônoma. Conjuntos limites. Campos gradientes. Campos Hamiltonianos. Campos no plano: órbitas periódicas e teorema de Poincaré-Bendixon. Órbitas periódicas hiperbólicas. Equação de Van der Pol. Referências: ARNOLD, V. - Equations Differentialles Ordinaires. Moscou, Ed. Mir, 1974. HIRSCH, M. e SMALE, S. - Differential Equations, Dynamical Systems and Linear Algebra. New York, Academic Press, 1974. PONTRYAGIN, L. S. - Ordinary Differential Equations. Reading, Mass., Addison-Wesley, 1969. SOTOMAYOR, J. - Lições de Equações Diferenciais Ordinárias. Rio de Janeiro, IMPA, Projeto Euclides, 1979. Marcelo Viana
Dynamical systems is a area of mathematics and science that studies how the state of systems change over time, in this module we will lay down the foundations to understanding dynamical systems as we talk about phase space and the simplest types of motion, transients and periodic motion, setting us up to approach the topic of nonlinear dynamical systems in the next module. This video is part of our Nonlinear systems introduction course take the full course at: https://www.udemy.com/nonlinear-systems-introduction Produced by Complexity lab: http://complexitylab.io For Transcriptions and downloads see: http://complexitylab.io/downloads-courses/ Transcription excerpt: Within science and mathematics, dynamics is the study of how things change with respect to time, as opposed to describing things simply in terms of their static properties the patterns we observe all around us in how the state of things change overtime is an alternative ways through which we can describe the phenomena we see in our world. A state space also called phase space is a model used within dynamic systems to capture this change in a system’s state overtime. A state space of a dynamical system is a two or possibly three-dimensional graph in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the state space. Now we can model the change in a system’s state in two ways, as continuous or discrete. Firstly as continues where the time interval between our measurements is negligibly small making it appear as one long continuum and this is done through the language of calculus. Calculus and differential equations have formed a key part of the language of modern science since the days of Newton and Leibniz. Differential equations are great for few elements they give us lots of information but they also become very complicated very quickly. On the other hand we can measure time as discrete meaning there is a discernable time interval between each measurement and we use what are called iterative maps to do this. Iterative maps give us less information but are much simpler and better suited to dealing with very many entities, where feedback is important. Where as differential equations are central to modern science iterative maps are central to the study of nonlinear systems and their dynamics as they allow us to take the output to the previous state of the system and feed it back into the next iteration, thus making them well designed to capture the feedback characteristic of nonlinear systems. The first type of motion we might encounter is simple transient motion, that is to say some system that gravitates towards a stable equilibrium and then stays there, such as putting a ball in a bowl it will role around for a short period before it settles at the point of least potential gravity, its so called equilibrium and then will just stay there until perturbed by some external force. Next we might see periodic motion, for example the motion of the planets around the sun is periodic. This type of periodic motion is of cause very predictable we can predict far out into the future and way back into the past when eclipses happen. In these systems small disturbances are often rectified and do not increase to alter the systems trajectory very much in the long run. The rising and receding motion of the tides or the change in traffic lights are also example of periodic motion. Whereas in our first type of motion the system simply moves towards its equilibrium point, in this second periodic motion it is more like it is cycling around some equilibrium. All dynamic systems require some input of energy to drive them, in physics they are referred to as dissipative systems as they are constantly dissipating the energy being inputted to the system in the form of motion or change. A system in this periodic motion is bound to its source of energy and its trajectory follows some periodic motion around it or towards and away from it. In our example of the planet’s orbit, it is following a periodic motion because of the gravitational force the sun exerts on it, if it were not for this driving force, the motion would cease to exist.
Dr. Eric R. Kaufmann faces challenges everyday in his research even though he investigates ordinary differential equations. The associate professor leads the...
PottersWheel is a program to create differential equation based models of dynamical systems like biochemical reaction networks. It can fit a model to experim...
Customers of our Hometown stores tell us they appreciate the vastly improved shopping experience and ...
The Sidney Herald 2015-04-19... technologies, track research and energy efficient traction power supply systems among other things.
DNA India 2015-04-19The Apple iPhone 6 Plus, on the other hand, differentiates itself through its sheer simplicity.
Business Day 2015-04-19It also gave CNET a hands-on peek at the new sets, complete with details on its HDR (high dynamic range) strategy.
CNET 2015-04-19Group Dynamics and Leadership has been a part of the department's curriculum since 2000.
Jamaica Observer 2015-04-19Help Cliff reach the top and get the eggs by completing equations ... equations with their example.
The Examiner 2015-04-19... virtual subnets - which in turn support multi-tenancy, VM mobility, and service differentiation.
noodls 2015-04-19"Yechury is a dynamic comrade, a good Parliamentarian ... He is a young, energetic and dynamic leader.
The Hindu 2015-04-19But when asked to solve the next equation, the Manitou Springs native didn't have a clue.
Colorado Springs Gazette 2015-04-19... the Transport Department deciding to bring in a colour code to differentiate autorickshaws with .
Big News Network 2015-04-19A team of scientists developed an apparatus capable of differentiating the breath of a healthy ...
Big News Network 2015-04-19The four polished stainless-steel tailpipes of the exhaust system provide a visual highlight.
noodls 2015-04-19This is an eclectic, almost postmodern piece for a very large orchestra, full of stylistic allusion and dynamic twists.
The Guardian 2015-04-19A differential equation is a mathematical equation for an unknown function of one or several variables that relates the values of the function itself and its derivatives of various orders. Differential equations play a prominent role in engineering, physics, economics, and other disciplines.
Differential equations arise in many areas of science and technology, specifically whenever a deterministic relation involving some continuously varying quantities (modeled by functions) and their rates of change in space and/or time (expressed as derivatives) is known or postulated. This is illustrated in classical mechanics, where the motion of a body is described by its position and velocity as the time value varies. Newton's laws allow one (given the position, velocity, acceleration and various forces acting on the body) to express these variables dynamically as a differential equation for the unknown position of the body as a function of time. In some cases, this differential equation (called an equation of motion) may be solved explicitly.
A dynamical system is a concept in mathematics where a fixed rule describes the time dependence of a point in a geometrical space. Examples include the mathematical models that describe the swinging of a clock pendulum, the flow of water in a pipe, and the number of fish each springtime in a lake.
At any given time a dynamical system has a state given by a set of real numbers (a vector) that can be represented by a point in an appropriate state space (a geometrical manifold). Small changes in the state of the system create small changes in the numbers. The evolution rule of the dynamical system is a fixed rule that describes what future states follow from the current state. The rule is deterministic; in other words, for a given time interval only one future state follows from the current state.
The concept of a dynamical system has its origins in Newtonian mechanics. There, as in other natural sciences and engineering disciplines, the evolution rule of dynamical systems is given implicitly by a relation that gives the state of the system only a short time into the future. (The relation is either a differential equation, difference equation or other time scale.) To determine the state for all future times requires iterating the relation many times—each advancing time a small step. The iteration procedure is referred to as solving the system or integrating the system. Once the system can be solved, given an initial point it is possible to determine all its future positions, a collection of points known as a trajectory or orbit.