physics

Computational Physics Basics: 2-Week Intensive Roadmap

2 weeks
0 Learners
kishor

An intensive two-week path from numerical foundations to verified simulations in computational physics. It emphasizes Python, NumPy, SciPy, error analysis, ODE/PDE solvers, Monte Carlo sampling, and reproducible verification artifacts.

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W1

Numerical Computing Foundations and Error Analysis

By the end of this module you will be able to construct vectorized numerical experiments, quantify floating-point and discretization error, and produce reproducible diagnostic plots with dimensionally consistent physical quantities.

4 videos117m
4 readings
4 topics
1 homework
Learn

Topics

1.1
Floating-Point Arithmetic and Numerical Error
26 minutes
1.2
Vectorized Scientific Computing with NumPy
56 minutes
1.3
Dimensional Analysis and Scaling
8 minutes
1.4
Scientific Visualization and Diagnostics
27 minutes
W2

Linear Algebra, Interpolation, and Nonlinear Solvers

By the end of this module you will be able to formulate computational physics problems as linear or nonlinear systems, select appropriate solvers, diagnose conditioning, and validate numerical solutions against analytic or benchmark results.

4 videos60m
4 readings
4 topics
1 homework
Learn
W3

Ordinary Differential Equations and Dynamical Systems

By the end of this module you will be able to implement and compare explicit, adaptive, and symplectic ODE integrators for physical systems, evaluate stability and conservation properties, and interpret phase-space diagnostics.

4 videos104m
4 readings
4 topics
1 homework
Learn
W4

Monte Carlo Sampling and Statistical Estimation

By the end of this module you will be able to implement Monte Carlo integration and Markov-chain sampling, quantify statistical uncertainty, diagnose autocorrelation, and compare sampled distributions with analytic statistical-physics targets.

4 videos54m
4 readings
4 topics
1 homework
Learn
W5

Partial Differential Equations and Finite-Difference Simulation

By the end of this module you will be able to discretize one-dimensional PDEs using finite differences, enforce common boundary conditions, analyze stability constraints, and perform grid-convergence studies for time-dependent simulations.

4 videos73m
4 readings
4 topics
1 homework
Learn
W6

Verification, Validation, Performance, and Reproducible Reporting

By the end of this module you will be able to verify numerical solvers with manufactured solutions, validate them against benchmarks, profile and optimize computational kernels, and package results into a reproducible computational physics report.

4 videos74m
4 readings
4 topics
1 homework
Learn
01

Learn

Watch curated videos and read study resources

02

Practice

Practice what you learned

03

Build Projects

Build projects using your new gained knowledge

04

Submit & Verify

Submit your project and get verified by our system

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