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Rapid Uncertainty Quantification on a Latent Field using Fisher Information
Abstract
Many inverse problems in physics infer an unobserved field from measurements connected to it through a governing equation. We combine Fisher information with a differentiable solver to quantify uncertainty in both the inferred field and its predicted observables, using a local Gaussian approximation without sampling the full parameter posterior. Applied to nuclear optical potentials, the method distinguishes features constrained by scattering measurements from those whose uncertainty remains set by the prior. Our flexible model improves agreement with held-out cross sections and achieves nearly nominal coverage with narrower predictive intervals than an established Bayesian optical model.
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BibTeX
@misc{daningburg2026rapid,
title = {{Rapid Uncertainty Quantification on a Latent Field using Fisher Information}},
author = {Karl Daningburg and A. E. Lovell and Arvind T. Mohan and R. O'Shaughnessy},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.29969},
eprint = {2609.29969},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.29969},
}
RIS
TY - UNPB
TI - Rapid Uncertainty Quantification on a Latent Field using Fisher Information
AU - Karl Daningburg
AU - A. E. Lovell
AU - Arvind T. Mohan
AU - R. O'Shaughnessy
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.29969
UR - https://arxiv.org/abs/2609.29969
AB - Many inverse problems in physics infer an unobserved field from measurements connected to it through a governing equation. We combine Fisher information with a differentiable solver to quantify uncertainty in both the inferred field and its predicted observables, using a local Gaussian approximation without sampling the full parameter posterior. Applied to nuclear optical potentials, the method distinguishes features constrained by scattering measurements from those whose uncertainty remains set by the prior. Our flexible model improves agreement with held-out cross sections and achieves nearly nominal coverage with narrower predictive intervals than an established Bayesian optical model.
ER -