OPEN COMPUTATIONAL PHYSICS

Build, compare, and
understand PDE solvers.

A shared home for open-source numerical software, neural PDE frameworks, mesh-free methods, reproducible benchmarks and practical learning.

Classical solvers · Neural PDE frameworks · Mesh-free methods

PHYSICS IN VIEW01 / FLUID DYNAMICS
Potential-flow streamlines around a circular cylinder.∇ · u = 0U∞
Different methods. Shared physics.Analytical potential flow · illustrative field
Speed / U∞
012

EXPLORE THE COMMONS

Models, solvers, and tools.

Explore established numerical software and the growing neural PDE ecosystem.

Independent projects · one place to understand them

CLASSICAL & MULTIPHYSICS

CFD PLATFORM

Open-source finite-volume CFD for fluids and continuum mechanics.

CFDFinite volume
FEM FRAMEWORK

Automated finite element computing through variational formulations.

FEMPython / C++
FEM LIBRARY

High-performance finite elements from laptops to heterogeneous HPC.

FEMCPU / GPU
COUPLING LIBRARY

Partitioned multiphysics coupling between existing solvers.

CouplingInteroperability

NEURAL PDE FRAMEWORKS

SCIENTIFIC ML

PINNs, DeepONets and multifidelity learning for differential equations.

PINNsOperator learning
PHYSICS-ML

Physics-ML models, training workflows and engineering examples.

Formerly ModulusModels + workflows
OPERATOR LEARNING

PyTorch implementations of Fourier and other neural operators.

FNOPyTorch

9 resources shownSample catalogue · upstream projects remain independent · no affiliation implied

HARDWARE, WITH EVIDENCE

Performance means
more than speed.

Compare time and memory at the accuracy your problem requires. From a laptop to a cluster, see the complete computational cost.

  • Shared problems and explicit error targets
  • Training, adaptation and inference separated
  • Reproducible software and hardware records
BENCHMARK EXPLORER

Time to an accepted solution

ILLUSTRATIVE DATA
Target relative L² error≤ 1%
Hardware classWall timePeak memory
0100 s

Example layout only. Values are synthetic—not measurements or hardware recommendations. Each row represents an illustrative solver configuration.

FROM PAPERS TO PRACTICE

Research that others can build on.

A proposed contribution pathway
01

Implement the idea

A method becomes a reusable module, with its assumptions, dependencies and original paper.

Code + provenance
02

Reproduce the result

Publish a runnable reference case and explain what matches—and what differs.

Reference case + evidence
03

Make it interoperable

Define interfaces and compatibility tests so others can compose it into new workflows.

Adapters + shared interfaces

A foundation for industry-specific tools. Build blade-design, thermal-analysis or structural-engineering applications on shared computational capabilities.

LEARN BY DOING

Build understanding alongside capability.

Proposed learning tracks