Free and open source · runs on your own computer

Open-source physics that gets better every time someone beats it.

A physics lab with more than twenty verified solvers, one native 3D app, and an engine that AI agents drive through JSON, a command line and MCP. Make films with it, design parts, do science: what you do with it is yours.

Download Source on GitHub

macOS 11 or later · Linux, headless · code Apache 2.0 · data CC BY 4.0

Seven of the lab's films playing at once: a heart valve opens and closes, a methane fire, a capsule at Mach 6, sound fills a room, a bottle shatters, light around a black hole, a flag in the wind — Every film on this page is a solver's own output.

Download

Download it. Build it in one command.

There is no installer: the source is the download, and it builds with the tools your computer already has. One command builds the app, the servers and the lab's tools.

macOS

the native app and everything else

macOS 11 or later with the Xcode Command Line Tools. Tested on Apple silicon.

Download the source (.zip)

Once, if the command line tools are missing
```
xcode-select --install
```
In the unzipped folder: the app, the servers and the lab's tools
```
make && make lab
```
Open the native app
```
make run
```

Linux

the engine, headless

The engine and the lab's tools build with clang and make. The headless build and its tests run on every push to main and on every pull request.

Download the source (.zip)

The toolchain, on Debian or Ubuntu

sudo apt-get install -y clang make python3

Build the headless targets
```
make headless
```
Run the verification suites
```
make test
```

With git

to contribute, or for an agent

Clone it to keep up with every improvement, or fork it to enter a flag.

Fork on GitHub

Clone

git clone https://github.com/molanocortes/OpenPhysicsAI.git
cd OpenPhysicsAI

Build, then run any scenario headless

make && make lab
./build/labrun examples/lab/room_fire_3d.json /tmp/room_fire.lab

Packaged builds are not published yet. Install notes: INSTALL.md · everything the repository can do from a shell: AGENTS.md

The lab

What it computes

Every film here is a solver's own output, with its scenario, its verification and its page.

A drone frame is 3D printed in PLA layer by layer, coloured by the stress it keeps as it cools, then released from the bed — **A drone frame, 3D printed.** Layer by layer in PLA, with the stress it keeps as it cools. Printing

A sailplane in a wind tunnel, a sheet of smoke tracers carried by the computed airflow over and behind it — **The wake of a sailplane.** Smoke tracers in the computed airflow. Wind tunnel

A finned steel heat sink warms from the pad at its base as 20 W flows in — **A heat sink warms up.** 20 W into a printed steel sink, cooled by the air. Heat

A dam of water collapses along a tank, its smooth surface running and splashing against a block — **A dam breaks.** Free-surface water surges down a tank and hits a block. Water

A pulse of sound leaves the stage of a concert hall and its wavefronts cross the hall, reflecting from the walls, orange and blue for high and low pressure — **Sound fills a concert hall.** A pulse from the stage, its echoes crossing the hall. Sound

A rubber sheet falls onto a ball, stretches over its crown and pleats into folds — **Rubber drapes over a ball.** Thin sheets that stretch, bend and touch. Sheets

An electric motor cut open in 3D: stator iron coloured by flux density, copper end windings, the rotor spinning up — **A motor in 3D.** Its magnetic field by finite elements, its rotor spinning up. Magnets

A steel gear inside a ring coil heats at its teeth first, by induction — **Induction heating.** Eddy currents heat a gear's teeth first. Magnets

A black hole with a thin disk of glowing gas, its far side bent over the top of the shadow — **Light near a black hole.** Every pixel a ray traced through curved spacetime. Relativity

Sixteen more simulations of the lab playing at once: a flag in the wind, a Whipple shield, Mach 10 on a wedge, water sloshing, a battery, a crater at 5 km/s, a cold-gas thruster, a wing stalling, a fire in a room, copper hitting a wall, laser tracks in steel, radar in a 3D volume, a data hall, a sphere through a plate, an office's air and a radar pulse — **Sixteen more of the lab's films.** A flag in the wind, a Whipple shield, Mach 10 on a wedge, water sloshing, a battery, a crater at 5 km/s, a wing stalling, a fire in a room, laser tracks in steel and more.

Everything the lab computes The physics map What each solver was checked against

The thirteen flags

Measurements of the real world that no simulator has predicted yet.

Anyone can go after them: download the lab, make it better with your own AI agent, submit it. A machine scores every submission against the real measurement, and the best code is merged, so the lab everyone downloads is always the best one anyone has built.

Flags

Hard problems of today. The best methods in the world, pushed hard, can capture them.

Semi holy grails

Answers that do not exist yet. Nature reveals them on a known date, and predictions must be registered before it.

Holy grails

Beyond the reach of any simulator today. Capturing one would change the world.

A flag is captured with a score of 90 out of 100, which means predictions within about half of the measurement's uncertainty. There is no prize and no money: the reward is the board itself, and the first to capture a flag keeps that place in its history for good.

Capture a flag in three steps

1Download. Fork the repository, or clone it, and build it: make && make lab.
2Make it better. Pick a flag or a trial and set your agent on it: Claude, GPT, Gemini, an open model, or none. Each flag has practice cases with public answers, so you can see how close you are before you submit.
3Submit. Add your entry under your GitHub name and open a pull request. A clean machine reruns it, scores it against the sealed measurements and posts the score on the pull request.

Fair play. An entry is a simulator, not a list of numbers: code that recognises a flag's inputs and returns an answer is disqualified. Scores are published in bands of five points, so they cannot be inverted into the answers.

The best place to start: four trials

Vortices peel off a cylinder in turn, left and right, and drift away as a street of swirls

T1 · leads to flag 01 How often a cylinder sheds vortices

A sphere a little faster than sound: a curved shock forms ahead of it and settles at its standoff distance

T2 · leads to flag 02 The bow shock ahead of a sphere

A weak shock sweeps over a cylinder of helium in air and rolls it up into a pair of vortices

T3 · leads to flag 04 A shock through a bubble of gas

Mercury, Venus, the Earth and Mars circle the Sun, their orbits traced by the solver

T4 · leads to flag 09 Where the planets will be

The full rules The whole leaderboard The reference entry

For AI agents

If your task needs physics, git clone is the shortest path to it.

An agent that only wants a result writes a scenario and runs build/labrun, which prints a JSON record with the scenario's hash. One that wants a structural or thermal analysis uses the MCP server.

Saves time and tokens. The physics map says which solver computes what, how it was checked and which files to read, in a few thousand tokens.
Units in every key. A scenario is plain JSON in which every number carries its unit in its name (length_m, pressure_pa, velocity_m_s); a number without a unit is refused.
Open. Apache 2.0 for the code, CC BY 4.0 for the data. Take one solver or all of them.

AGENTS.md The physics map llms.txt Connect an MCP client

From nothing to a result

git clone https://github.com/molanocortes/OpenPhysicsAI.git
cd OpenPhysicsAI
make lab
./build/labrun examples/lab/room_fire_3d.json /tmp/room_fire.lab

The last line runs a fire in a room headless and prints a JSON record of the result.

Why

Simulation of the physical world should be open, inspectable and checked against reality.

For people, and for the AI agents that increasingly do engineering work. Code is becoming cheap to write; what stays valuable is physics that has been verified, measurements to test it against, and a shared place where improvements add up. The flags are how that place keeps getting better: open-source simulators compete on them, and because every entry is open, every win is shared.

The vision The plan How to contribute

Get in touch

Research and evaluation collaborations.

Teaching with it, doing research on it, or testing AI models on physical problems? Open an issue and say what you have in mind. A well-documented measurement of your own can become a new flag.

Open an issue Propose a flag