Computational Physics
Numerical algorithms and high-performance computing for physical models.
Computational Physics is a topic within applied and computational. Numerical algorithms and high-performance computing for physical models. The area sits at the intersection of foundational theory and active research practice, and its methodology is shaped by a small set of canonical references that frame how problems are posed, how results are validated, and what counts as progress.
Work in this area progresses along several axes: the canonical theoretical framework, benchmark problems that calibrate methods against known answers, computational and experimental tooling that extends reach to larger or more complex systems, and frontier questions that current references either open up or partially answer. The references cited below illustrate these axes in different ways and together define the working vocabulary of the field.
Foundational references
The primary references for this topic establish the conceptual core and the standard problem set.
Computational Physics (Thijssen, 2007) is treated here as a primary reference for this area; its presentation of the subject is the canonical entry point for learners moving from prerequisites into independent work on computational physics.
Numerical Recipes: The Art of Scientific Computing (Press et al., 2007) is treated here as a primary reference for this area; its presentation of the subject is the canonical entry point for learners moving from prerequisites into independent work on computational physics.
Open methodological questions in computational physics include the precise scope of validity of the current dominant techniques, the integration of newer computational or experimental tools, and how this topic connects to neighbouring areas in the tree. Subsequent waves of editing will deepen these connections and add fresh frontier references as the literature evolves.
Prerequisites
Sources
- textbook · primary · 2007Computational Physicsthijssen-2007
- textbook · primary · 2007Numerical Recipes: The Art of Scientific Computingpress-2007, teukolsky-press-2007, vetterling-2007, flannery-2007
In context
Where this topic sits in the prerequisite graph. Click any node to jump.
Explore
- 01
Molecular Dynamics
Classical N-body simulation of atoms and molecules with empirical force fields.
- 02
Ab Initio Molecular Dynamics
First-principles dynamics combining electronic-structure calculations with nuclear motion.
- 03
Quantum Monte Carlo
Stochastic methods for ground-state and finite-temperature quantum many-body problems.
- 04
Tensor Network Methods
DMRG, MPS, PEPS, and MERA for low-entanglement many-body problems.
- 05
Finite Element Methods (Physics)
FEM discretization for elasticity, electromagnetism, and coupled PDE physics.
- 06
Particle-in-Cell Methods
Kinetic plasma and beam simulations using PIC discretization.
- 07
Machine Learning for Physics
Neural networks, surrogate models, and learned representations of physical systems.
- 08
Scientific Machine Learning
Physics-informed neural networks, operator learning, and differentiable simulation.
- 09
Inverse Problems (Physics)
Reconstruction of physical states and parameters from observational data.
- 10
Data-Driven Discovery of Equations
Sparse regression, symbolic regression, and operator-learning approaches to model discovery.
- 11
High-Performance Computing for Physics
Parallel architectures, GPUs, and exascale strategies for physics simulations.
- 12
Lattice Boltzmann Methods
Mesoscopic kinetic schemes for fluid and complex-flow simulation.
- 13
Neural-Network Wavefunctions
Variational ansätze parameterized by neural networks for quantum many-body problems.
- 14
Differentiable Physics Simulators
End-to-end differentiable simulators enabling gradient-based design and inference.
- 15
Uncertainty Quantification in Physics
Probabilistic propagation of input/model uncertainty through physical simulators.
Review this topic
This page was drafted by an agent and is waiting on expert review. Spotted a wrong prerequisite, a missing concept, a misattributed source, or a factual slip? Tell us — your review opens a tracked issue maintainers act on.