A variational autoencoder plus conditional latent diffusion model with a physics-informed residual corrector generates synthetic fields that improve symbolic regression recovery on sparse heat conduction, Navier-Stokes, and gravitational data.
Gsr: A generalized symbolic regression approach
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Data Enrichment for Symbolic Regression Using Diffusion Models
A variational autoencoder plus conditional latent diffusion model with a physics-informed residual corrector generates synthetic fields that improve symbolic regression recovery on sparse heat conduction, Navier-Stokes, and gravitational data.