Incorporating first-principles information into hybrid modeling structures: Comparing hybrid semi-parametric models with Physics-Informed Recurrent Neural Networks
Year: 2025
Type: article
Abstract: Funding Information: The authors of this work acknowledge the efforts of students and staff at the department of Chemical and Biochemical Engineering, DTU, for making process data available that was generated during courses in experimental unit operations for training of the hybrid models. We would also like to express gratitude to the Novo Nordisk Foundation for funding the digital infrastructure of pilot facilities through grant NNF19SA0035474. This work was supported by the Applied Molecular Biosciences Unit - UCIBIO which is financed by national funds from FCT (UIDB/04378/2020). Rui Oliveira and Monesh Kumar acknowledge funding from the European Union's Horizon 2020 research and innovation program under grant agreement no. 101099487\u2014BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer) Funding Information: The authors of this work acknowledge the efforts of students and staff at the department of Chemical and Biochemical Engineering, DTU, for making process data available that was generated during courses in experimental unit operations for training of the hybrid models. We would also like to express gratitude to the Novo Nordisk Foundation for funding the digital infrastructure of pilot facilities through grant NNF19SA0035474 . Funding Information: This work was supported by the Applied Molecular Biosciences Unit - UCIBIO which is financed by national funds from FCT ( UIDB/04378/2020 ). Rui Oliveira and Monesh Kumar acknowledge funding from the European Union\u2019s Horizon 2020 research and innovation program under grant agreement no. 101099487\u2014BioLaMer-HORIZON-EIC-2022-PATHFINDEROPEN-01 (BioLaMer) Publisher Copyright: © 2025 The Authors
Source: Computers & Chemical Engineering
Authors: Peter Jul-Rasmussen, Monesh Kumar, Jóse Pinto, Rui Oliveira, Xiaodong Liang, Jakob Kjøbsted Huusom
Institution: Technical University of Denmark
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,050
OpenAlex ID: W4409201748