יחידות הוראה

על הקורס - כללי

יחידות הוראה

  • Topics

    "Computers are useless. They can only give you answers."

                               - Pablo Picasso


    Lecture Zoom link

    Zoom link 2

    • Overview of general-purpose programming languages: Fortran, C, C++, Python
    • Programing basics: variables, control flow, data objects, algorithms
    • Overview of interpreted languages: Matlab, Mathematica
    • Numerics: accuracy, precision, stability, bottlenecks, computer structure
    • Numerical differentiation and integration; Runge Kutta methods
    • Interpolation and extrapolation: polynomial, spline, Laplace
    • Minimization and maximization: Brent, Newton, simulated annealing
    • Statistical description of data: modeling, comparing distributions
    • Monte Carlo simulations
    • (Time permitting) Ordinary differential equations: finite differences, shooting, relaxation
    • (Time permitting) Partial differential equations: reduction, relaxation, multi-grid



    Lecture Topic  Read  Blank notes
    		 Class notes Links & extra
    1
    		 Course Intro, programming languages, C basics, random walk
    		 tutorials draft1
    		Lec1
    		 Snake in 5 minutes; Dinosaurs in 3 minutes
    2
    		 Variable representation, precision, accuracy, loss of precision, recursion and differential equations
    		 NR1
    		  draft2Lec2
    Snake
    		 Roundoff disasters
    One-liners 2010
    3 Stability, scope of variables, arrays, passing variables by value/reference
    		 NR1
    		 draft3
    		Lec3
    		 C (de) referencing
    scope
    4 Dynamical programming, continued fractions, interpolation and extrapolation: intro and Neville method, searching an ordered table, reading NR codes
    		 NR3
    		 draft4Lec4
    		 Golden ratio
    Interpolation in Python
    5 Interpolation: rational, spline, and multiple dimensions; Integration: Newton-Cotes formulae
    		 NR3
    		 draft5
    		Lec5
    		 Integration in Python
    6 Newton-Cotes algorithms, Richardson extrapolation, Romberg open+closed methods, improper integrals 1
    		 NR4
    		 draft6
    		Lec6
    		 P vs. NP
    7 Improper integrals 2, orthogonal polynomials, Gaussian quadrature, integration in multiple dimensions
    		 NR4
    		 draft7
    		Lec7
    		 Monte Carlo examples
    8 Statistics recap, Monte Carlo, MC examples, variance reduction, fractals, sampling from a distribution
    NR
    7.6-7.8
    		 draft8
    		Lec8
    		 Mandelbrot zoom
    Logistic map
    9 quasi-MC, importance+stratified sampling, diffusion with interactions, working with data, moments of a distribution 1
    		 7.6-7.8
    NR 14
    		 draft9
    		Lec9
    		 Randomness
    10 Pitfalls in statistics, moments of a distribution 2, estimating the significance of sample statistics
    		 NR14
    		  
    11 Student t-test, MC in statistics, MC simulation as an integral, chi-squared test, KS-test
    		 NR14
    		  
    12 Maximal likelihood, Chi-squared minimization, root finding: bracketing, bisection, secant, false position, Brent, Newton-Raphson
    		 NR15
    		  
    13 Root finding in multiple dimensions: Newton-Raphson, Broyden; optimization: bracketing; ODEs: Euler, Runge-Kutta, explicit vs. implicit methods, stiff equations, adaptive step, Bulirsch-Stoer, shooting
    		 NR16
    		  


    • Textbooks

        • Numerical recipes, by William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery; Publisher: Cambridge University Press; 3 edition (September 10, 2007).
        • Computational physics, by Morten Hjorth Jensen; Publisher: CreateSpace Independent Publishing Platform (January 12, 2015)
        • A survey of computational physics, by Rubin H. Landau, Jos? P?ez, Cristian C. Bordeianu; Publisher: Princeton University Press; Har/Cdr edition (July 21, 2008)


      • Lecture, Tutorial and office hours

        Office Hours

        Name Day Hours Building/Room Email*
        Uri Keshet
        		 Wed17:00
        		 Zoom ukeshet@bgu.ac.il
        David Uzan Thu12:00 54/319 daviduz@post.bgu.ac.il

        Lecture/Tutorial

        Group What? Name Day Hours Zoom/Hybrid
        1 Lecture Uri Keshet Tue
        14:10 - 17:00
        		 בניין כתות לימוד [35] חדר 213
        11 Tutorial David Uzan Sun 18:10- 20:00
        		 גולדברגר [28] חדר 202
        * Please use the course staff email only for private issues, material related issues should be addressed in the course forum.

        • Class Exercises

        • Home Exercises

        • Recommendations

          Tools:

          • Lectures teach some C and Mathematica. Tutorials and assignments are in Python.
          • Visual C, Python, Mathematica, and Matlab are installed on most BGU computers.
          • Mathematica and Matlab are available within BGU campus and dorms on any mobile device through https://apps.bgu.ac.il/.
          • VPN to access the above - https://in.bgu.ac.il/computing/Pages/vpn-service.aspx ).
          • We mainly run Python using Google Collab or Jupyter. A more professional IDE: PyCharm. Feel free to use any workspace convenient for you.
          • C codes in this course are generally short, and can be compiled online e.g. on onlineGDB, ideone.com, or codepad.org.
          • You may wish to compile C on your machine, which would be faster, much more powerful, and independent of web connection. You may need to install a compatible free compiler. For Windows, use command-line compilation (invoke "Command Prompt" from the start button). You may need to install Visual Studio first. A lighter alternative is MSYS2 installation.
          • Before the course begins, we recommend refreshing your Python using some basic interactive guide (1, 2, 3, etc.), and becoming familiar with the Numpy, Scipy and Matplotlib libraries, which are used in the course. 
          • A C tutorial is also available and highly recommended.
          • Euler Project is a nice project that has plenty of mathematical problems you could solve only by programming, and could help you learn coding in a more interesting way.
          • Guido van Robot is a nice visual programming language that helps junior programmers understand the logic of coding. Recommended for students with no background in coding.


          • Course Policy

            • A weekly lecture (3 hours) and a weekly tutorial (2 hours).
            • A weekly problem set. Submit all (except maybe one) sets on time to attend the exam.
            • Self-grade your problem set within a week after the solutions are posted; we will sample the grades. Final grade: 80% exam, 20% problem sets.