PHY4605 develops the ability to turn a physics problem into a defensible computational experiment. The course emphasises physical modelling, method selection, algorithmic reasoning, validation, uncertainty, and interpretation. MATLAB and AI tools support implementation; they do not replace physical judgement.
physical model -> scale and units -> discretisation -> algorithm -> code -> error/uncertainty -> validation -> physical interpretation
MATLAB Onramp is a required self-learning prerequisite, but the teaching sequence assumes little dependable retention. Weeks 01–02 diagnose and repair the required MATLAB literacy through familiar physics before the course moves into numerical methods.
The course is planned as 13 instructional weeks plus Week 14 as an empty recovery buffer. If no scheduled session is lost, the buffer may be used for optional revision, remediation, consultation, or deferred capstone defence; it does not contain a new compulsory topic.
- Google Classroom is the official course hub and system of record for practical submissions, capstone milestones, individual lab-test submissions, and ungraded revision/reflection activities.
- Gemini Notebook / NotebookLM is the source-grounded Socratic AI tutor for approved course material and revision activities.
- MATLAB is the computational implementation and reproducibility environment.
- Test 1, Test 2, and the final examination remain AI-free under the assessment blueprint.
The standard AI-assisted revision pattern is initial attempt -> Socratic guidance -> revised reasoning -> independent validation -> reflection -> Classroom submission.
- Week 12 — Integrated method selection and capstone studio: learning note, validated lecture demonstration, completed 14-slide lecture deck and formative capstone studio.
- Week 09 — Data fitting and uncertainty: learning note, lecture demonstration, AI-enabled group practical, and lecture-slide outline; slide production has not started.
- Week 08 — ODE simulation: lecture deck, learning note, lecture demonstration, and AI-enabled group practical.
- Week 01: package pending rebuild
- Course topic and difficulty blueprint
- Course-material production blueprint
- Assessment blueprint
- Lecture slide design specification
- MATLAB Live Script design specification
- Learning note design specification
Student-facing instructional materials are stored in Week01/ through Week13/. Week14/ is reserved for buffer/recovery records and must not contain a new planned core package. Hidden .agent/ folders contain reproducibility sources, build material, and QA evidence.
The topic blueprint governs all future revisions. Earlier Week 01–05 materials remain archived under First Attempts/ as historical references only. Week 03 must move residual/rank/conditioning/power-balance work out of the Core route; Week 04's eigenproblem package is superseded by parameter sweeps and graph interpretation; and Week 05 must remove the extreme small-derivative case while moving advanced solver comparisons into optional material.
Owned and managed by Dr. Muhammad Khairul Adib Muhammad Yusof, Department of Physics, Faculty of Science, Universiti Putra Malaysia.