Bayesian transfer learning with Monte Carlo Markov Chains for kinetic modelling of pilot plant and industrial data

Authors

Per Julian Becker
Warumporn Pejpichestakul (ORCID: https://orcid.org/0000-0003-2964-286X)
Benoît Celse (ORCID: https://orcid.org/0000-0002-2503-6734)

Publication Details

Journal: Digital Chemical Engineering
Published: 2026-02-18
DOI: https://doi.org/10.1016/j.dche.2026.100295
Primary Topic: Gaussian Processes and Bayesian Inference
Type: article
Field-Weighted Citation Impact: 0.00

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article

Bayesian transfer learning with Monte Carlo Markov Chains for kinetic modelling of pilot plant and industrial data

Per Julian Becker, Warumporn Pejpichestakul, Benoît Celse

Feb 18, 2026

Digital Chemical Engineering

Gaussian Processes and Bayesian Inference

article

Bayesian transfer learning with Monte Carlo Markov Chains for kinetic modelling of pilot plant and industrial data

Per Julian Becker, Warumporn Pejpichestakul, Benoît Celse

February 18, 2026article en

Abstract

No abstract available for this paper.

Digital Chemical EngineeringVol. 19

Industry, innovation and infrastructure

Openalex Percentile: Top 54%

Gaussian Processes and Bayesian Inference

DOI Open Access PDF OpenAlex

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Bayesian transfer learning with Monte Carlo Markov Chains for kinetic modelling of pilot plant and industrial data — Per Julian Becker, Warumporn Pejpichestakul, et al. · Digital Chemical Engineering (2026) | TGRS Research Map | TGRS