Nuclear-physics-guided Gaussian Processes
Abstract
Gaussian Process Regression is a powerful nonparametric Bayesian method that provides both predictions and principled uncertainty estimates in closed form. The majority of past applications have relied on agnostic priors, but physics knowledge can be systematically encoded into Gaussian Processes through physically-motivated mean functions and kernels. We exploit this capability in the context of nuclear physics, applying physics-guided Gaussian Process Regression to three problems: nucleon-nucleon scattering phase shifts, mass excesses, and the finite-temperature equation of state of dense matter. In each case, we demonstrate that encoding known theoretical structures yields substantial and systematic improvements in interpolation accuracy, uncertainty calibration, and extrapolation reliability over agnostic baselines. Our results highlight that the design of the prior, and in particular the mean function, is key for obtaining a reliable and well-calibrated Gaussian Process Regression.
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BibTeX
@misc{sarmiento2026nuclearphysicsguided,
title = {{Nuclear-physics-guided Gaussian Processes}},
author = {Javier Rozalén Sarmiento and Hristijan Kochankovski and Arnau Rios and Àngels Ramos},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.11714},
eprint = {2609.11714},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.11714},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Nuclear-physics-guided Gaussian Processes
AU - Javier Rozalén Sarmiento
AU - Hristijan Kochankovski
AU - Arnau Rios
AU - Àngels Ramos
PY - 2026
DA - 2026/09/10
PB - arXiv
DO - 10.48550/arxiv.2609.11714
UR - https://arxiv.org/abs/2609.11714
AB - Gaussian Process Regression is a powerful nonparametric Bayesian method that provides both predictions and principled uncertainty estimates in closed form. The majority of past applications have relied on agnostic priors, but physics knowledge can be systematically encoded into Gaussian Processes through physically-motivated mean functions and kernels. We exploit this capability in the context of nuclear physics, applying physics-guided Gaussian Process Regression to three problems: nucleon-nucleon scattering phase shifts, mass excesses, and the finite-temperature equation of state of dense matter. In each case, we demonstrate that encoding known theoretical structures yields substantial and systematic improvements in interpolation accuracy, uncertainty calibration, and extrapolation reliability over agnostic baselines. Our results highlight that the design of the prior, and in particular the mean function, is key for obtaining a reliable and well-calibrated Gaussian Process Regression.
ER -