Search results “Numerical analysis and scientific computing”
Lec-1 Errors in Computation and Numerical Instability
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
Numerical Analysis (Error1): Absolute, Relative, Relative Percentage Error||lecture 1.1.1
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
Numerical Analysis: Bisection Method
Bisection Method explained with examples in a short time ;) Presenter: Atta Ulhaye
Views: 167688 Abdullah Sagheer
Intro to Numerical Computing with NumPy (Beginner) | SciPy 2018 Tutorial | Alex Chabot-Leclerc
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
Computational Methods for Numerical Relativity, Part 1 Frans Pretorius
Computational Methods for Numerical Relativity, Part 1 Frans Pretorius Princeton University July 16, 2009
JuliaCon 2018 | Numerical Analysis in Julia | Sheehan Olver
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
Numerical Methods
Numerical Methods
Views: 8385 Numerical Methods
Introduction to Numerical Computing with NumPy | SciPy 2017 Tutorial | Dillon Niederhut
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
Scott Sanderson - Foundations of Numerical Computing in Python - PyCon 2018
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
Bisection Method in Hindi
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"...
Lecture 1   Programming Basics-Computational Physics (Numerical Analysis)
This video lecture " Programming Basics " will help Physics Student to understand very begin for Numerical Analysis using C/C++.
Views: 2230 Salman Rayn
Scientific Computing Skills 5. Lecture 25.
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: 744 UCI Open
Numerical Libraries for Scientific Computing
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
Opportunities and challenges for numerical analysis in simulation – B. Wohlmuth – ICM2018
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
Computational Physics Lecture 20, Numerical Integration I
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
Numerical Analysis: Bisection Method - Secret Tips & Tricks - |Tutorial - 13|
Thanks for watching our video. Don't forget to LIKE SHARE & SUBSCRIBE OUR CHANNEL.
Views: 50055 Infiniti Classes
First steps with Julia for numerical computing - Bogumił Kamiński
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
Python-based scientific computing I
Speaker: Christopher Laumann (Boston University, U.S.A.) Summer School on Collective Behaviour in Quantum Matter | (smr 3235) 2018_08_29-16_00-smr3235
On convergence of numerical schemes for hyperbolic systems of conservation – S. Mishra – ICM2018
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
Bisection Method on Casio fx-991ES & fx-82MS Scientific Calculators_Very Easy!
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 IndiaStudyChannel- http://www.indiastudychannel.com/r/sujoy70.aspx Incoming Tags- z score statistics,find mean median mode statistics in ms excel,variance,standard deviation,linear regression,data processing,confidence intervals,average value,probability theory,binomial distribution,matrix,random numbers,error propagation,t statistics analysis,hypothesis testing,theorem,chi square,time series,data collection,sampling,p value,scatterplots,statistics lectures,statistics tutorials,business mathematics statistics,share stock market statistics in calculator,business analytics,GTA,continuous frequency distribution,statistics mathematics in real life,modal class,n is even,n is odd,median mean of series of numbers,math help,Sujoy Krishna Das,n+1/2 element,measurement of variation,measurement of central tendency,range of numbers,interquartile range,casio fx991,casio fx82,casio fx570,casio fx115es,casio 9860,casio 9750,casio 83gt,TI BAII+ financial,casio piano,casio calculator tricks and hacks,how to cheat in exam and not get caught,grouped interval data,equation of triangle rectangle curve parabola hyperbola,graph theory,operation research(OR),numerical methods,decision making,pie chart,bar graph,computer data analysis,histogram,statistics formula,matlab tutorial,find arithmetic mean geometric mean,find population standard deviation,find sample standard deviation,how to use a graphic calculator,pre algebra,pre calculus,absolute deviation,TI Nspire,TI 84 TI83 calculator tutorial,texas instruments calculator,grouped data,set theory,IIT JEE,AIEEE,GCSE,CAT,MAT,SAT,GMAT,MBBS,JELET,JEXPO,VOCLET,Indiastudychannel,IAS,IPS,IFS,GATE,B-Tech,M-Tech,AMIE,MBA,BBA,BCA,MCA,XAT,TOEFL,CBSE,ICSE,HS,WBUT,SSC,IUPAC,Narendra Modi,Sachin Tendulkar Farewell Speech,Dhoom 3,Arvind Kejriwal,maths revision,how to score good marks in exams,how to pass math exams easily,JEE 12th physics chemistry maths PCM,JEE maths shortcut techniques,quadratic equations,competition exams tips and ticks,competition maths,govt job,JEE KOTA,college math,mean value theorem,L hospital rule,tech guru awaaz,derivation,cryptography,iphone 5 fingerprint hack,crash course,CCNA,converting fractions,solve word problem,cipher,game theory,GDP,how to earn money online on youtube,demand curve,computer science,prime factorization,LCM & GCF,gauss elimination,vector,complex numbers,number systems,vector algebra,logarithm,trigonometry,organic chemistry,electrical math problem,eigen value eigen vectors,runge kutta,gauss jordan,simpson 1/3 3/8 trapezoidal rule,solved problem example,newton raphson,interpolation,integration,differentiation,regula falsi,programming,algorithm,gauss seidal,gauss jacobi,taylor series,iteration,binary arithmetic,logic gates,matrix inverse,determinant of matrix,matrix calculator program,sex in ranchi,sex in kolkata,vogel approximation VAM optimization problem,North west NWCR,Matrix minima,Modi method,assignment problem,transportation problem,simplex,k map,boolean algebra,android,casio FC 200v 100v financial,management mathematics tutorials,net present value NPV,time value of money TVM,internal rate of return IRR Bond price,present value PV and future value FV of annuity casio,simple interest SI & compound interest CI casio,break even point,amortization calculation,HP 10b financial calculator,banking and money,income tax e filing,economics,finance,profit & loss,yield of investment bond,Sharp EL 735S,cash flow casio,re finance,insurance and financial planning,investment appraisal,shortcut keys,depreciation,discounting
Views: 155150 Sujoy Krishna Das
Bisection Method ll Numerical Methods with One Solved Problem ll GATE 2019 Engineering Mathematics
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 -------------------------------------------------------------------------------------------------------------- My Production Gear 1. Mobile Camera: http://amzn.to/2wbZPJt 2. Tripod: http://amzn.to/2xGD122 3. Shooting Light: http://amzn.to/2wiBgsw 4. Green Screen: http://amzn.to/2wiUPRA 5. Laptop for Editing: http://amzn.to/2wiUPRA ----------------------------------------------------------------------------------------------------------------- To get more updates about GATE 2019 Mechanical engineering video lectures please subscribe us on the following link Visit our Website for more GATE Material, Guidance, and Videos http://www.gatexplore.com/ Subscribe us on YouTube https://www.youtube.com/channel/UCPtzUejgvGILvdVQCA9EkRA Follow us on G+ https://plus.google.com/u/0/b/117088329234701586721 Follow us on Facebook https://www.facebook.com/gatexplore Follow us on Twitter https://twitter.com/GateChannel -~-~~-~~~-~~-~- Please watch: "GATE 2019 Result Out! Check Your GATE 2019 Result Here | GATE 2019 Result Kaise Dekhe" https://www.youtube.com/watch?v=HN7Vy1EH3CU -~-~~-~~~-~~-~-
Numerical Methods Part 1 (Basics) || Engineering Mathematics for GATE
Engineering Mathematics Book (Affiliate) : http://amzn.to/2wBGByf http://fkrt.it/dvAE6TuuuN _ Let's Donate for Great Purpose of providing Free Education https://goo.gl/rJ6qM8 ___ Subscribe our YouTube channel & Press the Bell icon https://goo.gl/GfdE5A ___ Like our Facebook page https://www.facebook.com/lectures4free/ ___ Follow us on twitter https://twitter.com/Jittu0106 ___ Download Lecture Notes from Link given below https://drive.google.com/open?id=1_ko71N-HDQQzxfLKKJ50MhM8PHio3xsW ___ 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 _ Digital Pen i Use (Affiliate) : http://amzn.to/2xghnCL http://fkrt.it/dYHobTuuuN Microphone i use (Affiliate) : http://amzn.to/2issB3s http://fkrt.it/Cp~Q9!NNNN _ 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: 45955 Lectures Tube
The Modern Lab Notebook: Scientific computing with Jupyter and Python.
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/
Christophe Prud'homme: High performance computing with Feel++: applications and numerical methods
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
Numerical Computing in JavaScript by Mikola Lysenko
Numerical Computing in JavaScript - Mikola Lysenko
Views: 2242 node.js
On effective numerical methods for phase-field models – Tao Tang – ICM2018
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
Computational Physics with python tutorials- Book Review. Python for physics
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
Simple & Easy process to learn all the methods of NUMERICAL METHOD. LIKE,SHARE & SUBSCRIBE.
Views: 221162 Infiniti Classes
Numerical & Scientific Computing with SciPy : Overview of Optim & Gradient Desc Methd | packtpub.com
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
Thanks for watching our video. Don't forget to LIKE SHARE & SUBSCRIBE OUR CHANNEL.
Views: 87490 Infiniti Classes
Computational Physics Lecture 22, Numerical Integration of ODEs
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
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
Introduction to Scientific Computing and Data Analysis
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
A Google Opportunity in Numerical Computing
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: 2382 GoogleTechTalks
MCSE004 (numerical and statistical computing)-Session 1
Numerical and statistical computing
Views: 10366 V-LRN Videos
Floating Point Representation In Numerical Techniques By Sarvesh Gupta
Floating Point Representation In Numerical Techniques for IGNOU BCA(BCS-054) and MCA(MCSE-004) students.
Views: 19671 academiQ
Analysis and methods for multiscale kinetic equations with uncertainties – S. Jin – ICM2018
Numerical Analysis and Scientific Computing | Mathematics in Science and Technology Invited Lecture 15.4 | 17.4 Mathematical analysis and numerical methods for multiscale kinetic equations with uncertainties Shi Jin Abstract: Kinetic modeling and computation face the challenges of multiple scales and uncertainties. Developing efficient multiscale computational methods, and quantifying uncertainties arising in their collision kernels or scattering coefficients, initial or boundary data, forcing terms, geometry, etc. have important engineering and industrial applications. In this article we will report our recent progress in the study of multiscale kinetic equations with uncertainties modelled by random inputs. We first study the mathematical properties of uncertain kinetic equations, including their regularity and long-time behavior in the random space, and sensitivity of their solutions with respect to the input and scaling parameters. Using the hypocoercivity of kinetic operators, we provide a general framework to study these mathematical properties for general class of linear and nonlinear kinetic equations in various asymptotic regimes. We then approximate these equations in random space by the stochastic Galerkin methods, study the numerical accuracy and long-time behavior of the methods, and furthermore, make the methods “stochastically asymptotic preserving”, in order to handle the multiple scales efficiently. ICM 2018 – International Congress of Mathematicians © www.icm2018.org
Views: 89 Rio ICM2018
important formula video link below https://youtu.be/sa25VBwFiic PDF format included in discription