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cs.LG2025
Bayesian Symbolic Regression via Posterior Sampling
Geoffrey F. Bomarito, Patrick E. Leser
Symbolic regression is a powerful tool for discovering governing equations directly from data, but its sensitivity to noise hinders its broader application. This paper introduces a…
cs.LG2025
Latent Generative Modeling of Random Fields from Limited Training Data
James E. Warner, Tristan A. Shah, Patrick E. Leser +3
The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying quantities such as heterogeneous…