Incorporating first-principles information into hybrid modeling structures: Comparing hybrid semi-parametric models with Physics-Informed Recurrent Neural Networks

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

Authors

Institutions

Publication Details

Journal
Computers & Chemical Engineering
Published
2025-04-06
DOI
https://doi.org/10.1016/j.compchemeng.2025.109119
Citations
5
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
6.62

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Incorporating first-principles information into hybrid modeling structures: Comparing hybrid semi-parametric models with Physics-Informed Recurrent Neural Networks

Monesh Kumar, Rui Oliveira, Xiaodong Liang, Jakob Kjøbsted Huusom et al.
5 citations
Computers & Chemical Engineering
Model Reduction and Neural Networks
6.62
article

Incorporating first-principles information into hybrid modeling structures: Comparing hybrid semi-parametric models with Physics-Informed Recurrent Neural Networks

Monesh Kumar, Rui Oliveira, Xiaodong Liang, Jakob Kjøbsted Huusom, Peter Jul‐Rasmussen, José M. Pinto
article en
5 citations

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

Computers & Chemical EngineeringVol. 199
Technical University of Denmark (DK)
Novo Nordisk Fonden, Applied Molecular Biosciences Unit, Horizon 2020 Framework Programme, Fundação para a Ciência e a Tecnologia
Openalex Percentile: Top 4%
Model Reduction and Neural Networks
6.62
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.