Estimating Cultural Heritage Processes Using Approximate Bayesian Computation

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dc.contributor.authorStolfi, Paolaen_US
dc.contributor.authorOnofri, Eliaen_US
dc.contributor.authorBretti, Gabriellaen_US
dc.contributor.editorCampana, Stefanoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorGuidi, Gabrieleen_US
dc.contributor.editorHegarty, Zackaryen_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRemondino, Fabioen_US
dc.date.accessioned2025-09-05T20:50:36Z
dc.date.available2025-09-05T20:50:36Z
dc.date.issued2025
dc.description.abstractUnderstanding water absorption dynamics in porous materials is crucial for the preservation of cultural heritage artifacts, particularly in assessing the risk of deterioration due to moisture. In this work, we propose a Bayesian framework for parameter estimation of differential models describing imbibition curves---\ie, the amount of water absorbed over time by a material. Due to the complexity of the forward models and the intractability of the likelihood function, we employ the Approximate Bayesian Computation methodology to infer the model parameters based on experimental data. The proposed approach enables a probabilistic characterization of parameter uncertainty. We validate the method using synthetic and real experimental data collected from materials commonly found in historical buildings and artworks. Results show that the inference method accurately captures the underlying absorption dynamics and can serve as a reliable tool to support preventive conservation strategies.en_US
dc.description.sectionheadersH2IOSC Project Development
dc.description.seriesinformationDigital Heritage
dc.identifier.doi10.2312/dh.20253258
dc.identifier.isbn978-3-03868-277-6
dc.identifier.pages9 pages
dc.identifier.urihttps://doi.org/10.2312/dh.20253258
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/dh20253258
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Mathematics of computing → Bayesian computation; Partial differential equations; Applied computing → Arts and humanities
dc.subjectMathematics of computing → Bayesian computation
dc.subjectPartial differential equations
dc.subjectApplied computing → Arts and humanities
dc.titleEstimating Cultural Heritage Processes Using Approximate Bayesian Computationen_US
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