Codex Simulates Black Hole Plasma

Astrophysicist Chi-kwan Chan is leveraging OpenAI Codex to push the boundaries of black hole simulations. The AI tool assists in refining and testing complex algorithms needed to model the behavior of plasma around these cosmic giants.

Chan, a researcher at the University of Arizona and Steward Observatory, focuses on supermassive black holes, key targets for testing Einstein's theory of general relativity. The Event Horizon Telescope collaboration, of which Chan is a part, aims to produce the first video of a black hole.

Simulating the plasma, superheated matter of electrons and ions, near black holes presents a significant computational challenge. In hot, diffuse environments, particles rarely collide and instead spiral along magnetic field lines, requiring simulations to track trillions of individual particle movements with extremely small timesteps.

AI Accelerates Algorithm Discovery

This particle-level detail has historically limited the realism of simulations, even on supercomputers. Chan suspected that new mathematical techniques could bypass these limitations by altering how particle motion is tracked, avoiding the need to calculate every tiny spiral directly.

Exploring these mathematical possibilities manually would be an arduous task. Chan turned to OpenAI Codex to help derive candidate algorithms and test them against known solutions. While not all generated algorithms are correct, Chan emphasizes that their testability is key. This approach, potentially similar to how OpenAI Codex accelerates finance reporting, allows for rapid exploration of novel ideas.

Unlike some AI systems that provide opaque results, Chan’s group uses Codex to propose and implement numerical schemes that can be inspected, tested, and understood. This rigorous scientific verification process, where an idea is accepted based on repeated testing rather than its origin, is why Chan sees AI as a powerful tool for scientific discovery.

If these Codex-assisted approaches prove successful, they could enable simulations of trillions of particles, offering unprecedented insights into physics previously beyond reach. This advancement could be as impactful as when Codex powers data science with interactive reports, transforming complex analysis.

Chan’s work highlights how AI, even with its known limitations, can accelerate scientific research by expanding the scope of exploration and testing hypotheses faster than traditional methods, potentially mirroring the broad impact seen when OpenAI Teams Up With Oracle Cloud to scale its infrastructure.