<ul><li>Data-driven modeling of dynamical systems is a crucial area of machine learning.</li><li>A new approach called direct semantic modeling is proposed, which predicts the behavior of a dynamical system directly from data.</li><li>This approach bypasses the need for complex post-hoc analysis and enhances the transparency and flexibility of the resulting models.</li><li>The direct semantic modeling approach simplifies the modeling pipeline and allows for intuitive inductive biases and direct editing of the model's behavior.</li></ul>

No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs

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