Search results “Numerical analysis and scientific computing”

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: 2342
The Julia Language

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: 112630
nptelhrd

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: 23902
Enthought

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: 3264
PyCon 2018

Bisection Method explained with examples in a short time ;)
Presenter: Atta Ulhaye

Views: 162948
Abdullah Sagheer

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: 97
Rio ICM2018

Numerical Computing in JavaScript - Mikola Lysenko

Views: 2105
node.js

Simple & Easy process to learn all the methods of NUMERICAL METHOD.
LIKE,SHARE & SUBSCRIBE.

Views: 188191
Infiniti Classes

Nick Higham is Royal Society Research Professor and Richardson Professor of Applied Mathematics at the University of Manchester. His research is in numerical linear algebra and ranges from theory to the development of algorithms and software, with a focus on accuracy and stability.

Views: 4597
The Julia Language

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: 2034
Sharcnet HPC

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: 30101
Enthought

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"...

Views: 466341
Bhagwan Singh Vishwakarma

Views: 125280
ANEESH DEOGHARIA

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...

Views: 2378
GoogleTechTalks

http://www.youtube.com/sujoyn70
https://www.youtube.com/playlist?list=PLHGJFOxCJ5Iwm8kTk52LAQ-_T0IMwZZHD
I'm Sujoy, and today I'll tell you how to solve a Bisection Method problem of Numerical Analysis using your Casio fx-991ES & fx-82MS scientific calculators! After watching this video,you'll amaze that how easy it is to solve Bisection Method problem using calculators!
Topics covered-
i) What is Bisection Method?
ii) Definition of Algebraic and Transcendental Equations.
iii) Determining the lower limit and upper limit of root.
iv) Programming scientific calculator for problem solving.
v) Doing the iterations on calculator.
vi) When to stop the calculation?
vii) Getting the final answer.
viii) Verifying the answer.
I make videos on Statistics,Numerical Methods,
Business & Financial Mathematics,Operation Research,Computer Science & Engineering(CSE),Android Application Reviews,India Travel & Tourism,Street Foods,Life Tips and many other topics.
And a series of videos showing how to use your scientific calculators Casio fx-991ES & fx-82MS to do maths easily.
If you like my video, please "like" it, and "subscribe" to my Youtube Channel- http://www.youtube.com/sujoyn70 ,that will encourage me to upload more videos,also you'll be notified by email whenever I upload a new video.
My Blog- http://www.sujoyn70.blogspot.com
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Views: 148718
Sujoy Krishna Das

Learn how truncation error is defined. You will be introduced to three examples of truncation error. For more videos and resources on this topic, please visit http://nm.mathforcollege.com/topics/sources_of_error.html

Views: 71717
numericalmethodsguy

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: 8239
PyData

This method (Muller's Method) helps to find the real root of the equation..
In this video you will get..
1- Working Rule / Steps of this method
2- Numerical on this method
For more videos
Subscribe
Bhai Bhai Tutorials
By- Harendra Sharma

Views: 6450
Bhai Bhai Tutorials

This video lecture " Secant Method in hindi" will help Engineering and Basic Science students to understand following topic of of Engineering-Mathematics:
1. concept and working rule of Secant 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"...

Views: 158613
Bhagwan Singh Vishwakarma

Numerical Analysis and Scientific Computing
Invited Lecture 15.8
Numerical mathematics of quasicrystals
Pingwen Zhang
Abstract: Quasicrystals are one kind of fascinating aperiodic structures, and give a strong impact on material science, solid state chemistry, condensed matter physics and soft matters. The theory of quasicrystals, included in aperiodic order, has grown rapidly in mathematical and physical areas over the past few decades. Many scientific problems have been explored with the efforts of physicists and mathematicians. However, there are still lots of open problems which might to be solved by the close collaboration of physicists, mathematicians and computational mathematicians. In this article, we would like to bridge the physical quasicrystals and mathematical quasicrystals from the perspective of numerical mathematics.
© International Congress of Mathematicians – ICM
www.icm2018.org

Views: 113
Rio ICM2018

This video lecture " Interpolation 01- Newton forward difference formula in hindi" will help Engineering and Basic Science students to understand following topic of Engineering-Mathematics:
1. Concept of interpolation and extrapolation
2. Formation of forward difference table
3. statement of Newton forward difference interpolation formula
4. two solved problem
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"...

Views: 517992
Bhagwan Singh Vishwakarma

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: 1139
Ernazar Abdikamalov

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 introduce NumPy arrays as the efficient structure to hold and to perform computations in Python with data.
• Understand why use NumPy arrays
• Learn how to define NumPy arrays
• Explore additional examples
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: 1750
Packt Video

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].
This video provides an overview of the entire course.
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: 4868
Packt Video

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: 109
Rio ICM2018

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: 364
Packt Video

The notebooks used in this session are available on github:
https://github.com/dpsanders/hands_on_julia Visit http://julialang.org/ to download Julia.

Views: 16569
The Julia Language

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: 4414
Python Programmer

This video lecture " Application of Partial Differentiation in Error and Approximation in Hindi" will help Engineering and Basic Science students to understand following topic of of Engineering-Mathematics:
1. What is error and how we ind its approximate value...?
2. types of error.
3. 03 solved Problems.
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"...

Views: 127311
Bhagwan Singh Vishwakarma

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: 113
Rio ICM2018

In this video we define a couple of technical terms such as the "norm of a matrix" and the condition number of a matrix. We also use the condition number to determine whether the matrix is Ill conditioned or well conditioned. ITS SIMPLE!!
Calculating the NORM of a Matrix:
STEP 1
Sum up the absolute values of each component on the corresponding rows.
STEP 2
Select the largest of the "Row sums" and use that as the 'norm' of you matrix.
Calculating the condition number of the given Matrix:
STEP 1
Find the inverse of the given matrix
STEP 2
Calculate the NORM of the INVERSE of the matrix
STEP 3
Multiply the norm of the inverse by the norm of the original matrix and this is the condition number
STEP 4
If the condition number is quite large then we have an ill conditioned matrix. If the condition number is close to 1, then our matrix is well conditioned.
check out the following link for the Crout's method:
https://www.youtube.com/watch?v=yYxwlnilEJs
and for Doolittle Method:
https://www.youtube.com/watch?v=jbeX2HCW6OE
For more videos and content please check out my website:
www.everythingeng.com
Be sure to follow me in Twitter and LinkedIn: Blake Tabian
Like, share and subscribe! There will be plenty more to come!

Views: 25103
Blake Tabian

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

Views: 2005
Salman Rayn

UCI Chem 5 Scientific Computing Skills (Fall 2012)
Lec 25. Scientific Computing Skills
View the complete course: http://ocw.uci.edu/courses/chem_5_scientific_computing_skills.html
Instructor: Douglas Tobias, Ph.D.
License: Creative Commons BY-NC-SA
Terms of Use: http://ocw.uci.edu/info.
More courses at http://ocw.uci.edu
Description: This course introduces students to the personal computing software used by chemists for managing and processing of data sets, plotting of graphs, symbolic and numerical manipulation of mathematical equations, and representing chemical reactions and chemical formulas.
Scientific Computing Skills (Chem 5) is part of OpenChem: http://ocw.uci.edu/collections/open_chemistry.html
This video is part of a 25-lecture undergraduate-level course titled "Scientific Computing Skills" taught at UC Irvine by Professor Douglas Tobias.
Recorded December 13, 2012.
Index of Topics:
0:03:44 Importing a File
0:06:18 Statistical Analysis
0:10:14 Do-Loop
0:16:18 Error Bar Plot
0:19:57 Computable Data Documentation
0:21:38 Chemical Data
0:34:31 Protein Data
0:40:31 Wolfram Demonstration Project
0:46:52 Downloading CDF
0:51:57 Other Topics on the Demonstration Project
0:53:34 CDF Player
Required attribution: Tobias, Douglas Ph.D. Chemistry 5 (UCI OpenCourseWare: University of California, Irvine), http://ocw.uci.edu/courses/chem_5_scientific_computing_skills.html. [Access date]. License: Creative Commons Attribution-ShareAlike 3.0 United States License (http://creativecommons.org/licenses/by-sa/3.0/us/deed.en_US).

Views: 728
UCI Open

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: 96692
Jacob Bishop

Bridges to community video chat

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© 2019 How to earn money on online without any investment

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