Offered to students admitted to Year 1 in ALL
Major/Minor ALL
Course Type
Offer in 2023 - 2024 Y N
Course Code PHYS4150
Date2023/09/24 21:23
Enquiry for Course Details
PHYS4150 Computational physics (6 credits) Academic Year 2023
Offering Department Physics Quota 24
Course Co-ordinator Dr Z Y Meng, Physics < zymeng@hku.hk >
Teachers Involved (Dr Z Y Meng,Physics)
Course Objectives This course shows the power of computational approach to solving physics and related problems, which is complimentary to the traditional experimental and theoretical approaches. Students are expected to spend a significant fraction of time in actual programming.  This is an elective course for the computational physics theme.  This is also an essential course for those who plan to pursue postgraduate studies in fields like computational physics, condensed matter physics, chemistry and engineering or work in related areas.
Course Contents & Topics Topics include: Introduction to computational physics; ordinary differential equation for classical physical problems; partial differential equation for classical and quantum problems; matrix method and exactly diagonalization for classical and quantum problems; Monte Carlo methods for statistical physics and quantum many-body physics; numerical methods for phase transitions and machine learning approaches to physics problems.
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 demonstrate knowledge in essential methods and techniques for numerical computation in physics
CLO 2 apply matrix method and other simulation methods to solve deterministic as well as probabilistic classical and quantum physical problems
CLO 3 use appropriate numerical method to solve the differential equations governing the dynamics of physical systems
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Pass in (MATH3301 or MATH3401 or MATH3403 or MATH3405 or PHYS2160 or PHYS3151) and (PHYS3350 or PHYS3351 or PHYS3450 or PHYS3550)
Course Status with Related Major/Minor /Professional Core 2023 Major in Physics ( Disciplinary Elective )
2023 Major in Physics (Intensive) ( Disciplinary Elective )
2023 Minor in Physics ( Disciplinary Elective )
2022 Major in Physics ( Disciplinary Elective )
2022 Major in Physics (Intensive) ( Disciplinary Elective )
2022 Minor in Physics ( Disciplinary Elective )
2021 Major in Physics ( Disciplinary Elective )
2021 Major in Physics (Intensive) ( Disciplinary Elective )
2021 Minor in Physics ( Disciplinary Elective )
2020 Major in Physics ( Disciplinary Elective )
2020 Major in Physics (Intensive) ( Disciplinary Elective )
2020 Minor in Physics ( Disciplinary Elective )
2019 Major in Physics ( Disciplinary Elective )
2019 Major in Physics (Intensive) ( 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        1st sem    Examination Dec     
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 12
Lectures 36
Tutorials 8
Reading / Self study 80
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Assignments 30 CLO 1,2,3
Examination 2-hour written exam 30 CLO 1,2,3
Presentation 20 CLO 1,2,3
Project report 20 CLO 1,2,3
Required/recommended reading
and online materials		 Lecture notes provided by Course Coordinator
A. Klein and A. Godunov, Introductory Computational Physics (CUP, 2nd ed., 2010)
T. Pang: An introduction to computational physics (CUP, 2nd ed., 2006)
J. M. Thijssen: Computational Physics (CUP, 2nd ed., 2007)
D. P. Landau and K. Binder: A guide to Monte Carlo simulations in statistical physics (CUP, 4th ed., 2014)
E. Alpaydin: Introduction to machine learning (MIT Press, 2nd ed., 2009)
M. Girolami and S. Rogers: A first course in machine learning (Taylor and Francis, 2nd ed., 2016)
Course Website http://moodle.hku.hk
Additional Course Information NIL
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