LLMsMedium impactFor DevOpenAI Blog · June 11, 2026
How an astrophysicist uses Codex to help simulate black holes
Astrophysicist Chi-kwan Chan uses OpenAI's Codex to write code for simulating black holes, facilitating advanced physics research and testing of general relativity.
Signal strength3.7/5·OpenAI Blog
Astrophysicist Chi-kwan Chan uses OpenAI's Codex to write code for simulating black holes, facilitating advanced physics research and testing of general relativity.
TL;DR
Astrophysicist Chi-kwan Chan uses OpenAI's Codex to write code for simulating black holes, facilitating advanced physics research and testing of general relativity.
What happened
Chi-kwan Chan leveraged Codex, an AI-based code generation model, to aid in building simulations of black holes. This approach accelerates coding tasks for complex astrophysical phenomena.
Why it matters
Using AI like Codex for scientific simulations lowers barriers for researchers, speeding up model development and enabling deeper exploration of fundamental physics theories.
Generating deep dive...
AI-powered analysis takes a few seconds
The bigger picture
This example illuminates the broader trajectory of AI tools moving beyond generic code assistance toward domain-specialized scientific computing. It signals that LLMs like Codex are maturing into powerful enablers for researchers working at the intersection of theory and simulation, reducing dependency on extensive coding expertise. The implications ripple across industries reliant on numerical modeling, where accelerating development directly translates into faster innovation cycles. More importantly, this signals a democratization of high-level scientific programming, potentially changing who can contribute meaningfully to fields like astrophysics. The success here presages a future where AI-assisted programming becomes a standard research toolkit component, prompting new collaborations between AI teams and scientific communities.
Technical deep dive
Codex operates by conditioning on natural language prompts that specify the desired computational outcomes, which in this application involves generating code to solve the equations governing black hole physics-such as the Kerr metric and magnetohydrodynamic equations for accretion disks. The model’s integration into this workflow requires an interface to translate astrophysical problems into expressed prompt engineering that maximizes Codex’s contextual understanding. Further, because simulations demand numerical stability and accuracy, Chan implemented a validation layer that tests generated code against known benchmarks and theoretical constraints. Architecturally, this application relies on iterating between Codex-generated scripts and manual refinement, highlighting that current LLM outputs still require domain oversight. Strategically, this suggests a hybrid human-AI authoring pattern rather than full automation. Future iterations might integrate Codex with specialized simulation frameworks or GPU-accelerated numerical libraries to scale performance while retaining AI-driven code generation benefits.
Real-world applications
1
Generating initial simulation code for modeling black hole event horizons and related relativistic effects.
2
Automating repetitive coding tasks in astrophysical simulations, such as mesh grid generation and parameter sweeping.
3
Assisting researchers in generating visualization scripts for interpreting gravitational wave data from simulations.
4
Rapidly prototyping alternative gravity models by modifying simulation code structures using AI suggestions.
What to do now
Experiment with Codex or similar LLMs in your existing scientific coding projects to understand prompt engineering nuances for domain-specific problems.
Develop validation frameworks that can automatically check AI-generated simulation code against theoretical physics constraints and numerical accuracy.
Collaborate with domain experts to build prompt libraries tailored to your field’s computational challenges to improve code generation quality.
Integrate AI code generation into iterative research workflows, treating LLM outputs as drafts that require expert validation and refinement.