AI assistant tackles cosmology's trickiest math ⚡ экспресс
The growing volume of algorithmic computation in theoretical physics—often conceptually simple but tedious and full of subtle snares—fuels the hunt for new tools. This study explores whether large language models (LLMs) integrated with a computer algebra system (CAS) and relevant context can reliably tackle such tasks. The Claude model was connected to Maple and applied to computing cosmological perturbations in modified gravity theories. The paper demonstrates the current capabilities, typical breakdowns, and ways to fix them. The key takeaway: a cutting-edge LLM, fed ready-made examples, successfully handles most test problems.
The Universe's expansion is accelerating, and maybe gravity doesn't work the way we think. Physicists come up with alternative theories, but to test them, they need to calculate how ripples after the Big Bang grew into galaxies. Such calculations involve long equations with hundreds of terms, where it's easy to get lost. Georges Lemaître and Edwin Hubble laid the groundwork, but now a machine takes over the grunt work.
The neural network Claude, hooked up to the math package Maple, works like a robot chef: you give it a recipe (the initial theory), it measures out ingredients (simplifies formulas), mixes them (substitutes expressions), and bakes up predictions. The physicist just has to taste-test the result—compare it with observations. On the problem of cosmic perturbations, after a couple of examples, the AI handled most new cases, but sometimes slipped up—flipping signs or dropping terms.
Such an assistant could speed up the hunt for the secrets of dark energy many times over—or even show how the expansion can happen without it. For now, human oversight is still essential, but it's clear: the future of cosmology demands a symbiosis of living and machine intelligence.
🎯 The first calculations of cosmic ripples were done with pencil and paper. One arithmetic slip, and a discovery could be delayed by a decade.