Offered to students admitted to Year 1 in ALL
Major/Minor ALL
Course Type
Offer in 2023 - 2024 Y N
Course Code PHYS2160
Date2023/09/24 23:00
Enquiry for Course Details
PHYS2160 Introductory computational physics (6 credits) Academic Year 2023
Offering Department Physics Quota 30
Course Co-ordinator Dr F K Chow, Physics < judychow@hku.hk >
Teachers Involved (Dr F K Chow,Physics)
Course Objectives This is one of the second level courses in our series of courses that introduces problem solving, mathematical and computational skill sets that are commonly used in the study of university-level physics. This course introduces computational tools, techniques, and methods in physics and related fields using the Python programming language. Students are expected to spend a substantial amount of time in writing computer programs to solve physical problems. After completion, interested students may take the sequel courses PHYS3151, PHYS4150 or PHYS4151 to further their studies in computational physics.
Course Contents & Topics Topics include: basics of computer programming; Python programming for physicists; introduction to object-oriented programming in Python; scientific programming with Matplotlib, NumPy, and SciPy; simple error analysis in scientific programming; solution of non-linear equations with application in quantum physics; Calculus and numerical methods with relevant examples in physics; numerical solution of ordinary differential equations with application to pendulum motion.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 demonstrate knowledge in basic computational techniques and methods in physics
CLO 2 apply Python programming language and relevant packages to solve simple physical problems
CLO 3 employ appropriate numerical methods for solving ordinary differential equations that commonly arise in physics
CLO 4 review the numerical methods for simulation of various physical systems
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in MATH1013 or MATH1821 or MATH1851 or PHYS1150
Course Status with Related Major/Minor /Professional Core 2023 Major in Physics ( Disciplinary Elective )
2023 Major in Physics (Intensive) ( Disciplinary Elective )
2023 Minor in Astronomy ( Disciplinary Elective )
2023 Minor in Physics ( Disciplinary Elective )
2022 Major in Physics ( Disciplinary Elective )
2022 Major in Physics (Intensive) ( Disciplinary Elective )
2022 Minor in Astronomy ( Disciplinary Elective )
2022 Minor in Physics ( Disciplinary Elective )
2021 Major in Physics ( Disciplinary Elective )
2021 Major in Physics (Intensive) ( Disciplinary Elective )
2021 Minor in Astronomy ( Disciplinary Elective )
2021 Minor in Physics ( Disciplinary Elective )
2020 Major in Physics ( Disciplinary Elective )
2020 Major in Physics (Intensive) ( Disciplinary Elective )
2020 Minor in Astronomy ( Disciplinary Elective )
2020 Minor in Physics ( Disciplinary Elective )
2019 Major in Physics ( Disciplinary Elective )
2019 Major in Physics (Intensive) ( Disciplinary Elective )
2019 Minor in Astronomy ( Disciplinary Elective )
2019 Minor in Physics ( Disciplinary Elective )
Course to PLO Mapping 2023 Major in Physics < PLO 1,2,3,4 >
2023 Major in Physics (Intensive) < PLO 1,2,3,4 >
2022 Major in Physics < PLO 1,2,3,4 >
2022 Major in Physics (Intensive) < PLO 1,2,3,4 >
2021 Major in Physics < PLO 1,2,3,4 >
2021 Major in Physics (Intensive) < PLO 1,2,3,4 >
2020 Major in Physics < PLO 1,2,3,4 >
2020 Major in Physics (Intensive) < PLO 1,2,3,4 >
2019 Major in Physics < PLO 1,2,3,4 >
2019 Major in Physics (Intensive) < PLO 1,2,3,4 >
Offer in 2023 - 2024 Y        2nd sem    Examination May     
Offer in 2024 - 2025 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills required for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills. Apply highly effective lab skills and techniques. Critical use of data and results to draw appropriate and insightful conclusions.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills. Apply effective lab skills and techniques. Correct use of data of results to draw appropriate conclusions.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills. Apply moderately effective lab skills and techniques. Mostly correct but some erroneous use of data and results to draw appropriate conclusions.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills. Apply partially effective lab skills and techniques. Limited ability to use data and results to draw appropriate conclusions.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective. Apply minimally effective or ineffective lab skills and techniques. Misuse of data and results and/or unable to draw appropriate conclusions.
Communication-intensive Course N
Course Type Lecture with laboratory component course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Laboratory 18
Lectures 27
Project work 12
Tutorials 3
Reading / Self study 64
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Examination 2-hour written exam 50 CLO 1,2,3,4
Laboratory reports 20 CLO 1,2
Presentation 10 CLO 1,2,3,4
Project report 20 CLO 1,2,3,4
Required/recommended reading
and online materials		 Lecture notes provided by Course Coordinator
Christian Hills: Learning Scientific Programming with Python (Cambridge University Press, 2016)
Andi Klein and Alexander Godunov: Introductory computational physics (Cambridge University Press, 2010)
Mark Newman: Computational Physics (CreateSpace Independent Publishing Platform, 2012)
Hans Petter Langtangen: A Primer on Scientific Programming with Python (Springer, 2016, 5th edition)
Matt A. Wood: Python and Matplotlib Essentials for Scientists and Engineers (Morgan & Claypool, 2015)
Course Website http://moodle.hku.hk
Additional Course Information NIL
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