---

When reality is too messy: Teaching AI to learn physics from noisy, real-world data

---

Most simulation models learn from pristine lab conditions, but real proteins and weather systems are chaotic, wobbling between states. This work uses something already hiding in your data—covariance patterns—as a geometric compass to keep the AI's internal map accurate even when the world is blurry.

This means we can build faster, more reliable computer models of complex systems (drug interactions, climate patterns) from the kind of imperfect data scientists actually have.

---