מבוא לפיסיקה חישובית, 2023
Topic outline
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Zoom Meetings (online classes) External tool
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"Computers are useless. They can only give you answers."
- Pablo Picasso
- 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, Mathematica
demo including neural networkstutorials draft1 Lec1 Networks2.nb 2 C workflow, Variable representation, precision, accuracy, loss of precision, random walk NR1 draft2 Lec2 Roundoff disasters
One-liners 20103 Mathematical recursion, numerical stability, functions, scope of variables, passing variables by value/reference NR1 draft3 Lec3 Variable scope 4 Pointers, dynamical programming, continued fractions, interpolation and extrapolation: intro NR3 draft4 Lec4 C referencing
Golden ratio5 Searching an ordered list, reading NR codes, Interpolation: polynomial, rational, spline; P vs. NP NR3 draft5 Lec5 Interpolation in Python, P vs. NP 6 Interpolation in multiple dimensions; Integration: Newton-Cotes formulae, Richardson extrapolation NR4 draft6 Lec6 Quadrature in Mathematica 7 Newton-Cotes algorithms, Richardson extrapolation, Romberg's method, improper integrals NR4 draft7 Lec7 Quadrature in Python -
- 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)
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Office Hours
Name Day Hours Building/Room Email* Uri Keshet Thursday 17:00 54/214 ukeshet@bgu.ac.il David Uzan Wednesday 14:00 54/319 daviduz@post.bgu.ac.il Lecture/Tutorial
Group What? Name Day Hours Building/Room 1 Lecture Uri Keshet Tue 14:10 - 17:0032/206 11 Tutorial David Uzan Thu 15:10- 17:00 28/205
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Give yourself an appropriate grade
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Give yourself an appropriate grade
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Give yourself an appropriate grade
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Give yourself an appropriate grade
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Give yourself an appropriate grade
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Give yourself an appropriate grade
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Give yourself an appropriate grade
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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.
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- 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: 70% exam, 30% problem sets.