A Latent Space Optimization Approach for Symbolic Discovery of Dynamical Models
The framework solves symbolic regression by transforming discrete expression search into a continuous optimization problem. It uses a Variational Autoencoder (VAE) to map symbolic expressions into a continuous latent space, and then employs Bayesian Optimization with Gaussian Processes to efficiently search this space. This method outperforms traditional methods like deterministic MINLP formulations and evolutionary algorithms in discovering dynamical models.