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Φ-SO : Physical Symbolic Optimization - Learning Physics from Data 🧠 The Physical Symbolic Optimization package uses deep reinforcement learning to discover physical laws from data. Here is Φ-SO discovering the analytical expression of a damped harmonic oscillator.

Jousef Murad

19,828 subscribers

433,149 次观看 • 2 年前 •via X (Twitter)

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9 条评论

Jousef Murad 的头像

Jousef Murad2 年前

🌎 Repo: 👉 Paper:

Luke Barnes 的头像

Luke Barnes2 年前

This isn't physics. It's curve fitting. If you want to do physics, start by having the code discover the underlying differential equation. Handy, though.

Rasmus Nordström 的头像

Rasmus Nordström2 年前

ELI5 please, is this just brute forcing different formulas to see what fits that data or is it more magic to it?

Moritz Zaiss 的头像

Moritz Zaiss2 年前

Nice, I love these approaches! A differentiable physicist that doesn't need coffee. Check out the work of @GMartius in this context!

Jiwoong Lee 的头像

Jiwoong Lee2 年前

Would it be more straightforward to optimize in frequency domain? Curious.

Luis Tobon 的头像

Luis Tobon2 年前

Try Sindy.

Wassim Tenachi 的头像

Wassim Tenachi2 年前

Thanks for sharing my work, glad to see people excited about this ! 😊

Unser Kampf : notre combat 的头像

Unser Kampf : notre combat2 年前

How many points are tested ? 4k+1 ?

Unser Kampf : notre combat 的头像

Unser Kampf : notre combat2 年前

Il like very much. Your works completes mine.

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