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Views: 37 Ebony

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Lecture series on Numerical Methods and Computation by Prof.S.R.K.Iyengar, Department of Mathematics, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 114255 nptelhrd

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Numerical analysis tutorial Video Chapter: Error Topic: Absolute Error, Relative Error, Relative Percentage Error.. *Though 22/7 is not equal to pi. its also an approximation. this example is only to understand the concept.
Views: 48291 LucidConcept

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Views: 776 Eric Davishahl

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Bisection Method explained with examples in a short time ;) Presenter: Atta Ulhaye
Views: 167688 Abdullah Sagheer

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Views: 24320 Enthought

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NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algorithms clearly and efficiently. We'll introduce basic array syntax and array indexing, review some of the available mathematical functions in NumPy, and discuss how to write your own routines. Along the way, we'll learn just enough about matplotlib to display results from our examples. See tutorial materials here: https://scipy2018.scipy.org/ehome/299527/648136/ See the full SciPy 2018 playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gd-tNhm79CNMe_qvi35PgUR
Views: 38487 Enthought

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Computational Methods for Numerical Relativity, Part 1 Frans Pretorius Princeton University July 16, 2009

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This workshop brings together four speakers on different topics in numerical analysis, to demonstrate the strengths of Julia’s approach to scientific computing in function approximation, differential equations, fast transformations, validated numerics, and linear algebra.
Views: 2628 The Julia Language

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Numerical Methods
Views: 8385 Numerical Methods

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Views: 22 Sarah Collier

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Views: 140886 ANEESH DEOGHARIA

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Tutorial materials found here: https://scipy2017.scipy.org/ehome/220975/493423/ NumPy provides Python with a powerful array processing library and an elegant syntax that is well suited to expressing computational algorithms clearly and efficiently. We'll introduce basic array syntax and array indexing, review some of the available mathematical functions in numpy, and discuss how to write your own routines. Along the way, we'll learn just enough of matplotlib to display results from our examples.
Views: 24452 Enthought

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Speaker: Scott Sanderson Python is one of the world's most popular programming languages for numerical computing. In areas of application like physical simulation, signal processing, predictive analytics, and more, engineers and data scientists increasingly use Python as their primary tool for working with numerical large-scale data. Despite this diversity of application domains, almost all numerical programming in Python builds upon a small foundation of libraries. In particular, the `numpy.ndarray` is the core data structure for the entire PyData ecosystem, and the `numpy` library provides many of the foundational algorithms used to power more domain-specific libraries. The goal of this tutorial is to provide an introduction to numpy -- how it works, how it's used, and what problems it aims to solve. In particular, we will focus on building up students' mental model of how numpy works and how **idiomatic** usage of numpy allows us to implement algorithms much more efficiently than is possible in pure Python. Slides can be found at: https://speakerdeck.com/pycon2018 and https://github.com/PyCon/2018-slides
Views: 3601 PyCon 2018

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This video lecture " Bisection Method in Hindi" will help Engineering and Basic Science students to understand following topic of of Engineering-Mathematics: 1. concept and working rule of Bisection method 2. one solved example soon we will upload next video. For any query and feedback, please write us at: [email protected] OR call us at: +919301197409(Hike number) For latest updates subscribe our channel " Bhagwan Singh Vishwakarma" or join us on Facebook "Maths Bhopal"...

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This video lecture " Programming Basics " will help Physics Student to understand very begin for Numerical Analysis using C/C++.
Views: 2230 Salman Rayn

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Views: 744 UCI Open

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In this seminar we begin with an overview of the numerical software packages developed in the past decades and the latest developments. We will take a look at the libraries and packages recently installed on SHARCNET systems. We will then focus on a number of selected libraries and packages and walk through them with examples. In particular, we would like to discuss the linear algebra packages in a collection of open source and proprietary libraries; the fastest FFT library FFTW; the peer reviewed C++ library Boost.Numeric.Odeint, intel ODE solvers and other packages for solving ordinary differential equations (ODEs); the packages for solving linear and nonlinear (partial differential) equations (PDEs); the packages for optimization problems; the GNU scientific library (GSL); the parallel random number generator SPRNG, and the arbitrary precision packages such as MPFUN. We will present simple examples in both C/C++ and Fortran for problems accessible to a general audience with a sound knowledge in numerical methods and working experience of C/C++ and/or Fortran. ________________________________________­_____ This webinar was presented by Ge Baolai (SHARCNET) on April 1st, 2015 as a part of a series of regular biweekly webinars ran by SHARCNET. The webinars cover different high performance computing (HPC) topics, are approximately 45 minutes in length, and are delivered by experts in the relevant fields. Further details can be found on this web page: https://www.sharcnet.ca/help/index.php/Online_Seminars SHARCNET is a consortium of 18 Canadian academic institutions who share a network of high performance computers (http://www.sharcnet.ca). SHARCNET is a part of Compute Ontario (http://computeontario.ca/) and Compute Canada (https://computecanada.ca).
Views: 2094 Sharcnet HPC

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Numerical Analysis and Scientific Computing Invited Lecture 15.6 Opportunities and challenges for numerical analysis in large-scale simulation Barbara Wohlmuth Abstract: For centuries, many important theories and models of physical phenomena have been characterized by partial differential equations. But numerical methods for approximating such equations have only appeared over the last half century with the emergence of computers. Principal among these methods are finite elements. Today major challenges remain with the advent of modern computer architectures and the need for massively parallel algorithms. Traditionally the assembling of finite element matrices and the computation of many a posteriori error estimators is obtained by local operators and thus regarded as cheap and of optimal order complexity. However optimal order complexity is not necessarily equivalent to short run-times, and memory traffic may slow down the execution considerably. Here we discuss several ingredients, such as discretization and solver, for efficient approximations of coupled multi-physics problems. Surrogate finite element operators allow for a fast on-the-fly computation of the stiffness matrix entries in a matrix free setting. A variational crime analysis then yields two-scale a priori estimates. To balance the dominating components, the scheme is enriched by an adaptive steering based on a hierarchical decomposition of the residual. Several numerical examples illustrate the need for a performance aware numerical analysis. ICM 2018 – International Congress of Mathematicians © www.icm2018.org
Views: 106 Rio ICM2018

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In this lecture, we discuss the basic methods for numerical integration. We start with the Newton-Cotes formulae and describe in detail the trapezoidal rule and Simpson's 1/3 and 3/8 rules. Then, we discuss the Monte Carlo integration method. This video was created to accompany the course "Computational Physics (PHYS 270)" taught in the spring of 2017 at Nazarbayev University.
Views: 1286 Ernazar Abdikamalov

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Thanks for watching our video. Don't forget to LIKE SHARE & SUBSCRIBE OUR CHANNEL.
Views: 50055 Infiniti Classes

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Description The talk is an introduction to programming in Julia and it constructed around hands-on example of its usage. The material is selected in order to help the participants learn when Julia can be a language of choice for solving practical problems. No previous knowledge of Julia is required. Similarities and differences to Python and R will be discussed. Abstract Julia programming language tries to solve problem of delivering a flexible dynamic language, appropriate for scientific and numerical computing, with performance comparable to traditional statically-typed languages. The talk will discuss in particular: 1. How Julia was designed to allow C-level execution speed? 2. What are benefits and costs of such design? 3. Performance of Julia vs R and Python; in particular comparison to Numba . In order to keep the talk practical all concepts will be discussed using a typical numerical computing task from quantitative finance - pricing of Asian options. The presentation will be concluded by discussion of current state of Julia language ecosystem and its readiness for deployment in production solutions. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 9375 PyData

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Speaker: Christopher Laumann (Boston University, U.S.A.) Summer School on Collective Behaviour in Quantum Matter | (smr 3235) 2018_08_29-16_00-smr3235

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Numerical Analysis and Scientific Computing Invited Lecture 15.9 On the convergence of numerical schemes for hyperbolic systems of conservation laws Siddhartha Mishra Abstract: A large variety of efficient numerical methods, of the finite volume, finite difference and DG type, have been developed for approximating hyperbolic systems of conservation laws. However, very few rigorous convergence results for these methods are available. We survey the state of the art on this crucial question of numerical analysis by summarizing classical results of convergence to entropy solutions for scalar conservation laws. Very recent results on convergence of ensemble Monte Carlo methods to the measure-valued and statistical solutions of multi-dimensional systems of conservation laws are also presented. © International Congress of Mathematicians – ICM www.icm2018.org
Views: 154 Rio ICM2018

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Views: 155150 Sujoy Krishna Das

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Views: 45955 Lectures Tube

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You can think of this as three or four tutorial seminars rolled into one: no need to watch it in one sitting, and no need to watch it all! It starts with the basics, and builds up from there. It’s intended for people who have some Python programming experience, but know little about the libraries that have become so popular recently in numerical analysis and data science. Or for people who may even have used them — pasted some code into a Jupyter notebook as part of a college exercise, say — but not really understood what was going on behind the scenes. This is for you. I hope you find it useful! Menu: 00:00 Preface and Intro 11:26 Jupyter Notebooks intro 30:32 The basics of NumPy 56:31 Playing with images 1:25:22 Playing with audio 1:35:37 Playing with Pandas More information at https://statusq.org/archives/2019/04/11/8946/

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Abstract: I will review (some of) the HPC solution strategies developed in Feel++. We present our advances in developing a language specific to partial differential equations embedded in C++. We have been developing the Feel++ framework (Finite Element method Embedded Language in C++) to the point where it allows to use a very wide range of Galerkin methods and advanced numerical methods such as domain decomposition methods including mortar and three fields methods, fictitious domain methods or certified reduced basis. We shall present an overview of the various ingredients as well as some illustrations. The ingredients include a very expressive embedded language, seamless interpolation, mesh adaption, seamless parallelisation. As to the illustrations, they exercise the versatility of the framework either by allowing the development and/or numerical verification of (new) mathematical methods or the development of large multi-physics applications - e.g. fluid-structure interaction using either an Arbitrary Lagrangian Eulerian formulation or a levelset based one; high field magnets modeling which involves electro-thermal, magnetostatics, mechanical and thermo-hydraulics model; ... - The range of users span from mechanical engineers in industry, physicists in complex fluids, computer scientists in biomedical applications to applied mathematicians thanks to the shared common mathematical embedded language hiding linear algebra and computer science complexities. Recording during the CEMRACS 2016: "Numerical challenges in parallel scientific computing" the July 26, 2016 at the Centre International de Rencontres Mathématiques (Marseille, France) Filmmaker: Guillaume Hennenfent Find this video and other talks given by worldwide mathematicians on CIRM's Audiovisual Mathematics Library: http://library.cirm-math.fr. And discover all its functionalities: - Chapter markers and keywords to watch the parts of your choice in the video - Videos enriched with abstracts, bibliographies, Mathematics Subject Classification - Multi-criteria search by author, title, tags, mathematical area

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Numerical Computing in JavaScript - Mikola Lysenko
Views: 2242 node.js

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Numerical Analysis and Scientific Computing Invited Lecture 15.10 On effective numerical methods for phase-field models Tao Tang Abstract: In this article, we overview recent developments of modern computational methods for the approximate solution of phase-field problems. The main difficulty for developing a numerical method for phase field equations is a severe stability restriction on the time step due to nonlinearity and high order differential terms. It is known that the phase field models satisfy a nonlinear stability relationship called gradient stability, usually expressed as a time-decreasing free-energy functional. This property has been used recently to derive numerical schemes that inherit the gradient stability. The first part of the article will discuss implicit-explicit time discretizations which satisfy the energy stability. The second part is to discuss time-adaptive strategies for solving the phase-field problems, which is motivated by the observation that the energy functionals decay with time smoothly except at a few ‘critical’ time levels. The classical operator-splitting method is a useful tool in time discrtization. In the final part, we will provide some preliminary results using operator-splitting approach. © International Congress of Mathematicians – ICM www.icm2018.org
Views: 168 Rio ICM2018

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This excellent book on computational physics with python tutorials covers, computing software basics, python libraries, errors and uncertainties in computations, Monte Carlo methods - randomness, walks, Differentiation and integration, matrix computing using numpy, data fitting, solving ordinary differential equations, fourier analysis, non linear dynamics, fractals and statistical growth models, molecular dynamics, partial differential equations, heat equation, wave equation and several other topics including Feynman path integrals. It covers the maths and the code. It is an exhaustive book for computational physics and will teach you many very useful approaches to scientific coding using python for physics. But it it very expensive. If this has been useful, then consider giving your support by buying me a coffee https://ko-fi.com/pythonprogrammer Buy the book (Affiliate link) https://amzn.to/2KGuL9C If you want to learn python, I have a free course here on my YouTube channel https://www.youtube.com/playlist?list=PLtb2Lf-cJ_AWhtJE6Rb5oWf02RC2qVU-J
Views: 5284 Python Programmer

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Simple & Easy process to learn all the methods of NUMERICAL METHOD. LIKE,SHARE & SUBSCRIBE.
Views: 221162 Infiniti Classes

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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2pM7jfZ]. The aim of this video is to find solutions of optimization problems in Python. • Get introduced to the basic ideas about optimization and the functionality offered to solve these kind of problems • Learn the relevance of solving optimization problems and their importance in Science and Engineering • Study the theoretical digression on the gradient descent method For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 406 Packt Video

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Thanks for watching our video. Don't forget to LIKE SHARE & SUBSCRIBE OUR CHANNEL.
Views: 87490 Infiniti Classes

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In this lecture, we introduce the basic methods for solving ordinary differential equations. We discuss the Euler's method, Heun's method, and the midpoint method. This video was created to accompany the course "Computational Physics (PHYS 270)" taught in the spring of 2017 at Nazarbayev University.
Views: 1212 Ernazar Abdikamalov

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What is COMPUTATIONAL SCIENCE? What does COMPUTATIONAL SCIENCE mean? COMPUTATIONAL SCIENCE meaning - COMPUTATIONAL SCIENCE definition - COMPUTATIONAL SCIENCE explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Computational Science (also scientific computing or scientific computation (SC)) is a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems. Computational science fuses three distinct elements: Algorithms (numerical and non-numerical) and modeling and simulation software developed to solve science (e.g., biological, physical, and social), engineering, and humanities problems Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components needed to solve computationally demanding problems The computing infrastructure that supports both the science and engineering problem solving and the developmental computer and information science In practical use, it is typically the application of computer simulation and other forms of computation from numerical analysis and theoretical computer science to solve problems in various scientific disciplines. The field is different from theory and laboratory experiment which are the traditional forms of science and engineering. The scientific computing approach is to gain understanding, mainly through the analysis of mathematical models implemented on computers. Scientists and engineers develop computer programs, application software, that model systems being studied and run these programs with various sets of input parameters. In some cases, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms. Numerical analysis is an important underpinning for techniques used in computational science.
Views: 2081 The Audiopedia

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Learn more at: http://www.springer.com/978-3-319-30254-6. MATLAB codes used for all of the numerical methods are available from author's website. Extensive coverage of optimization methods including regression, both principal and independent component analysis, and variational calculus. Directed towards problem solving that incorporates the mathematical foundations of the subject. Main Discipline: Mathematics
Views: 130 SpringerVideos

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Google Tech Talks July 25, 2007 ABSTRACT The largest changes in computing with machines continues to be in speed and ease of access. Google is the leader in providing new and better tools to access computing in ways that increase user's productivity. However, in most practical applications, the set of safe numerical computations with floating-point arithmetic remains empty. Macsyma, Reduce, Mathematica, and Maple have expanded the use of computers to do symbolic mathematics. However, numerical computing and symbolic mathematics have diverged into their own domains because numerical computing with floating-point numbers is not safe. This talk answers the following questions: * How computing...

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Numerical and statistical computing
Views: 10366 V-LRN Videos

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Floating Point Representation In Numerical Techniques for IGNOU BCA(BCS-054) and MCA(MCSE-004) students.