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: 113410
nptelhrd

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: 2454
The Julia Language

Subject:Information Technology
Paper: Numerical methods

Views: 16405
Vidya-mitra

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: 45651
LucidConcept

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: 33500
Enthought

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: 141
Rio ICM2018

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: 24130
Enthought

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

Views: 165507
Abdullah Sagheer

Views: 133204
ANEESH DEOGHARIA

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

Views: 202711
Infiniti Classes

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: 1231
Ernazar Abdikamalov

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: 102
Rio ICM2018

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: 3411
PyCon 2018

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: 494626
Bhagwan Singh Vishwakarma

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: 2047
Sharcnet HPC

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: 8677
PyData

Numerical Computing in JavaScript - Mikola Lysenko

Views: 2169
node.js

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: 129
SpringerVideos

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: 169039
Bhagwan Singh Vishwakarma

Engineering Mathematics Book (Affiliate) :
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This GATE lecture of engineering mathematics on topic "Numerical Methods Part 1 (Basics)" will help the GATE aspirants engineering students to understand following topic:
Introduction
Analytical methods
Numerical Methods
Application of Numerical Methods
Advantage & Disadvantage
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This Gate Lectures of engineering mathematics is very useful for preparation of GATE exam. This lecture is also useful in Linear Algebra – matrix and determinants, Differential Equations, Probability & Statistics, Complex Variables, Calculus, Numerical Methods, Transform theory, Gate Lecture for Electronics & Communication (EC), Gate Lecture for Mechanical Engineering (ME), Gate Lecture for Computer Science & Engineering (CSE), Gate Lecture for Electrical Engineering (EE), Gate Lecture for Civil .
Engineering (CE), Gate Lecture for Information Technology (IT) .

Views: 44363
Lectures Tube

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.
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Views: 152197
Sujoy Krishna Das

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)
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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: 4765
Python Programmer

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: 380
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: 5103
Packt Video

http://www.gatexplore.com/ Bisection Method ll Numerical Methods with One Solved Problem ll GATE 2019 Engineering Mathematics
Download PDF notes here
http://www.gatexplore.com/bisection-method/
For More update about GATE 2019 News follow below link
http://www.gatexplore.com/
Topics Covered in this video
1) Concept of Bisection method
2) Step/Procedure of Bisection method
3) Problem on Bisection Method
4) Solved Problem
5) Intermediate value theorem
6) Bisection Method PDF
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Views: 16336
GATE Lectures by Dishank

This is an audio version of the Wikipedia Article:
https://en.wikipedia.org/wiki/Computational_science
00:02:21 1 The computational scientist
00:05:49 2 Applications of computational science
00:06:06 2.1 Urban complex systems
00:07:24 2.2 Computational finance
00:08:48 2.3 Computational biology
00:11:05 2.4 Complex systems theory
00:11:29 2.5 Computational science in engineering
00:13:02 3 Methods and algorithms
00:15:08 4 Conferences and journals
00:16:01 5 Education
00:17:48 6 Related fields
00:17:58 7 See also
Listening is a more natural way of learning, when compared to reading. Written language only began at around 3200 BC, but spoken language has existed long ago.
Learning by listening is a great way to:
- increases imagination and understanding
- improves your listening skills
- improves your own spoken accent
- learn while on the move
- reduce eye strain
Now learn the vast amount of general knowledge available on Wikipedia through audio (audio article). You could even learn subconsciously by playing the audio while you are sleeping! If you are planning to listen a lot, you could try using a bone conduction headphone, or a standard speaker instead of an earphone.
Listen on Google Assistant through Extra Audio:
https://assistant.google.com/services/invoke/uid/0000001a130b3f91
Other Wikipedia audio articles at:
https://www.youtube.com/results?search_query=wikipedia+tts
Upload your own Wikipedia articles through:
https://github.com/nodef/wikipedia-tts
Speaking Rate: 0.7255192502884492
Voice name: en-AU-Wavenet-C
"I cannot teach anybody anything, I can only make them think."
- Socrates
SUMMARY
=======
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. It is an area of science which spans many disciplines, but at its core it involves the development of models and simulations to understand natural systems.
Algorithms (numerical and non-numerical): mathematical models, computational models, and computer simulations 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 scienceIn 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. The essence of computational science is the application of numerical algorithms and/or computational mathematics. In some cases, these models require massive amounts of calculations (usually floating-point) and are often executed on supercomputers or distributed computing platforms.

Views: 1
wikipedia tts

This video lecture " Regula Falsi Method in hindi(Part-I)" will help Engineering and Basic Science students to understand following topic of of Engineering-Mathematics:
1. concept of Regula Falsi Method
2. working rule of Regula Falsi Method
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: 364918
Bhagwan Singh Vishwakarma

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: 132579
Bhagwan Singh Vishwakarma

This video lecture "Numerical Integration -Trapezoidal rule, Simpson's rule and weddle's rule in hindi " will help Engineering and Basic Science students to understand following topic of Engineering-Mathematics:
1. concept of Numerical integration
2. formula for Trapezoidal rule, Simpson's rule and Weddle's rule
3. 3 solved example
4. one 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: 521339
Bhagwan Singh Vishwakarma

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: 7677
Bhai Bhai Tutorials

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: 16641
The Julia Language

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: 2379
GoogleTechTalks

This video has no audio (on purpose).

Views: 5765
Abraham Smith

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: 555489
Bhagwan Singh Vishwakarma