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picture1_Computational Physics With Python Pdf 191667 | Mp468 2021s1 Lectureslides00a


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File: Computational Physics With Python Pdf 191667 | Mp468 2021s1 Lectureslides00a
computational physics 2 administrative lecturer me lectures thursdays 12noon 1pm labs thursdays 2pm 4pm mark distribution cont assessment quizes assignments 30 exam 70 mp468p project dual department students oct may ...

icon picture PDF Filetype PDF | Posted on 04 Feb 2023 | 2 years ago
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  Computational Physics 2
   Administrative
   Lecturer:   me
   Lectures:   Thursdays 12noon - 1pm
   Labs:       Thursdays 2pm - 4pm
   Mark distribution:   Cont.assessment
                        (= quizes + assignments) −→ 30%
                        Exam −→ 70%
   MP468P Project:     (dual-department students)    Oct–May
   Overview of lecture slides 00
   1  Content/overview of module
   2  The unix/linux command line
   3  Programming language(s) — choice of python
   4  Changing landscape of computational physics
   5  Things not covered
   6  The practice of scientific computing
   7  You should...
   Computational Physics 2: Content
   Module topics
        Random numbers and stochastic processes
        Monte Carlo methods
        Linear algebra
        (Linear sets of equations, matrix decompositions, eigenvalues)
        Minimisation / Optimisation
        Partial differential equations (PDEs)
        +ODEboundary value problems
        “Soft skills”:    unix/linux command-line, python
  Computational Physics 2: Specialties
   This module is a bit different...
                       (Math(linear algebra, PDE’s)
       Components of     Statistics (random processes, probability)
                         Computer Science (Algorithms)
       ‘Lab’ work
       May turn out to be the most useful subject for your future
       The most ‘modern’ module.
       (Numerics with python — less than 20 years old)
       Some aspects will become outdated in a few years.
       (Programming tools, workflow, etc. Not the principles.)
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...Computational physics administrative lecturer me lectures thursdays noon pm labs mark distribution cont assessment quizes assignments exam mpp project dual department students oct may overview of lecture slides content module the unix linux command line programming language s choice python changing landscape things not covered practice scientic computing you should topics random numbers and stochastic processes monte carlo methods linear algebra sets equations matrix decompositions eigenvalues minimisation optimisation partial dierential pdes odeboundary value problems soft skills specialties this is a bit dierent math pde components statistics probability computer science algorithms lab work turn out to be most useful subject for your future modern numerics with less than years old some aspects will become outdated in few tools workow etc principles...

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