AI RESEARCH
BITS for GAPS: Bayesian Information-Theoretic Sampling for hierarchical GAussian Process Surrogates
arXiv CS.LG
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ArXi:2511.16815v2 Announce Type: replace-cross We nstrate the framework's utility for hybrid modeling with a vapor-liquid equilibrium. Specifically, we build a surrogate model for latent activity coefficients in a binary mixture. We construct a hybrid model by embedding the surrogate into an extended form of Raoult's law. This hybrid model then informs distillation design. This shows how partial physical knowledge can be translated into a hierarchical Gaussian process surrogate.