SJEPA learns latent predictive states whose transitions are compact symbolic laws with a regularized neural residual, and demonstrates simpler, less divergent pendulum dynamics than post-hoc symbolic fitting.
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SJEPA: Learning Elegant Latent Dynamics with Hybrid Symbolic-Neural Predictors
SJEPA learns latent predictive states whose transitions are compact symbolic laws with a regularized neural residual, and demonstrates simpler, less divergent pendulum dynamics than post-hoc symbolic fitting.