Computational physics
Graduate · Physics
Syllabus focus
Standard syllabus · STEM / applied
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Standard syllabus
Advanced numerics
- Spectral methods and FFT-based solvers
- Finite element method foundations
- Adaptive mesh refinement strategies
- Implicit schemes for stiff ODEs/PDEs
- Monte Carlo: variance reduction and MCMC
Physics simulations
- Density functional theory pipelines
- Molecular dynamics with thermostats
- Lattice gauge theory computations
- N-body gravity with tree codes
- Radiative transfer solvers
Validation and analysis
- Convergence studies and grid independence
- Uncertainty quantification in simulation
- Benchmark suites and regression tests
- Visualization of vector and tensor fields
- Reproducible workflows and containers
STEM / applied
High-performance computing
- MPI domain decomposition
- OpenMP and hybrid parallelism
- GPU kernels for stencil and FFT workloads
- I/O at scale: HDF5 and parallel filesystems
- Job scheduling on clusters and clouds
Machine learning interfaces
- Neural network potentials for MD
- Surrogate models for expensive solvers
- Physics-informed neural networks cautions
- Automated experiment design with BO
- Ethics of ML in scientific inference
Research software
- Contributing to open-source physics codes
- Software citation and credit norms
- Pair programming in theory–computation teams
- Performance profiling with vendor tools
- Publishing simulation supplements
Notes
Topics reflect common graduate physics core and elective syllabi at US universities. Sequencing and emphasis vary between one- and two-semester treatments.