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An end-to-end strategy for recovering a free-form potential from a snapshot of stellar coordinates
New large observational surveys such as Gaia are leading us into an era of data abundance, offering unprecedented opportunities to discover new physical laws through the power of machine learning.
Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws
Here we present $\Phi$-SO, a Physical Symbolic Optimization framework for recovering analytical symbolic expressions from physics data using deep reinforcement learning techniques by learning units constraints.
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