On the public citation graph

Physics‐informed neural networks for biopharmaceutical cultivation processes: Consideration of varying process parameter settings

https://doi.org/10.1002/bit.28851
0 signed · 65 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/biot.201300385openalex-snapshot
Hybrid modeling for quality by design and PAT‐benefits and challenges of applications in biopharmaceutical industry
inferredcites →https://doi.org/10.1002/biot.201700395openalex-snapshot
Workflow for Target‐Oriented Parametrization of an Enhanced Mechanistic Cell Culture Model
inferredcites →https://doi.org/10.1002/biot.201900172openalex-snapshot
Bioprocessing in the Digital Age: The Role of Process Models
inferredcites →https://doi.org/10.1002/biot.201900551openalex-snapshot
Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization
inferredcites →https://doi.org/10.1002/biot.202200381openalex-snapshot
Comparison of mechanistic and hybrid modeling approaches for characterization of a CHO cultivation process: Requirements, pitfalls and solution paths
inferredcites →https://doi.org/10.1002/bit.26960openalex-snapshot
Model‐based optimization of antibody galactosylation in CHO cell culture
inferredcites →https://doi.org/10.1002/bit.27097openalex-snapshot
A new generation of predictive models: The added value of hybrid models for manufacturing processes of therapeutic proteins
inferredcites →https://doi.org/10.1002/bit.27120openalex-snapshot
Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization
inferredcites →https://doi.org/10.1002/bit.28503openalex-snapshot
Hybrid modeling in bioprocess dynamics: Structural variabilities, implementation strategies, and practical challenges
inferredcites →https://doi.org/10.1002/elsc.201600037openalex-snapshot
Intensified design of experiments for upstream bioreactors
inferredcites →https://doi.org/10.1007/10_2010_98openalex-snapshot
Advancing Biopharmaceutical Process Development by System-Level Data Analysis and Integration of Omics Data
inferredcites →https://doi.org/10.1007/10_2020_152openalex-snapshot
Mechanistic Mathematical Models as a Basis for Digital Twins
inferredcites →https://doi.org/10.1007/978-0-387-45528-0openalex-snapshot
Pattern Recognition and Machine Learning
inferredcites →https://doi.org/10.1007/978-3-030-54630-4_13openalex-snapshot
Eukaryotic Expression Systems for Upstream Processing of Monoclonal Antibodies
inferredcites →https://doi.org/10.1007/s00449-016-1557-1openalex-snapshot
Hybrid modeling as a QbD/PAT tool in process development: an industrial E. coli case study
inferredcites →https://doi.org/10.1007/s00449-019-02089-7openalex-snapshot
Model-assisted Design of Experiments as a concept for knowledge-based bioprocess development
inferredcites →https://doi.org/10.1007/s10915-022-01939-zopenalex-snapshot
Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next
inferredcites →https://doi.org/10.1007/s11831-020-09405-5openalex-snapshot
Multiscale Modeling Meets Machine Learning: What Can We Learn?
inferredcites →https://doi.org/10.1016/b978-0-12-818634-3.50005-9openalex-snapshot
Towards Model-Based Optimization for Quality by Design in Biotherapeutics Production
inferredcites →https://doi.org/10.1016/b978-0-444-63965-3.50475-xopenalex-snapshot
How to Use Mechanistic Metabolic Modeling to Ensure High Quality Glycoprotein Production
inferredcites →https://doi.org/10.1016/j.biotechadv.2020.107552openalex-snapshot
Mammalian cell culture for production of recombinant proteins: A review of the critical steps in their biomanufacturing
inferredcites →https://doi.org/10.1016/j.btre.2021.e00640openalex-snapshot
Generic and specific recurrent neural network models: Applications for large and small scale biopharmaceutical upstream processes
inferredcites →https://doi.org/10.1016/j.ces.2022.117512openalex-snapshot
Kinetic model development and Bayesian uncertainty quantification for the complete reduction of Fe-based oxygen carriers with CH4, CO, and H2 for chemical looping combustion
inferredcites →https://doi.org/10.1016/j.cma.2020.113552openalex-snapshot
SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
inferredcites →https://doi.org/10.1016/j.cma.2024.116813openalex-snapshot
Respecting causality for training physics-informed neural networks
inferredcites →https://doi.org/10.1016/j.coche.2018.08.010openalex-snapshot
Mechanistic modeling and applications for CHO cell culture development and production
inferredcites →https://doi.org/10.1016/j.coche.2021.100691openalex-snapshot
Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses
inferredcites →https://doi.org/10.1016/j.coche.2021.100715openalex-snapshot
Hybrid modeling — a key enabler towards realizing digital twins in biopharma?
inferredcites →https://doi.org/10.1016/j.coisb.2021.03.005openalex-snapshot
On structural and practical identifiability
inferredcites →https://doi.org/10.1016/j.drudis.2018.01.039openalex-snapshot
The rise of deep learning in drug discovery
inferredcites →https://doi.org/10.1016/j.jcp.2017.11.039openalex-snapshot
Hidden physics models: Machine learning of nonlinear partial differential equations
inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
inferredcites →https://doi.org/10.1016/j.tibtech.2020.05.008openalex-snapshot
Towards a Digital Bioprocess Replica: Computational Approaches in Biopharmaceutical Development and Manufacturing
inferredcites →https://doi.org/10.1016/j.tibtech.2021.04.003openalex-snapshot
Validation Is Not Verification: Precise Terminology and Scientific Methods in Bioprocess Modeling
inferredcites →https://doi.org/10.1021/acs.jpca.3c06265openalex-snapshot
Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions
inferredcites →https://doi.org/10.1021/bp020035yopenalex-snapshot
Adaptive, Model‐Based Control by the Open‐Loop‐Feedback‐Optimal (OLFO) Controller for the Effective Fed‐Batch Cultivation of Hybridoma Cells
inferredcites →https://doi.org/10.1038/323533a0openalex-snapshot
Learning representations by back-propagating errors
inferredcites →https://doi.org/10.1038/nbt1026openalex-snapshot
Production of recombinant protein therapeutics in cultivated mammalian cells
inferredcites →https://doi.org/10.1038/s42254-021-00314-5openalex-snapshot
Physics-informed machine learning
inferredcites →https://doi.org/10.1080/03639045.2017.1291672openalex-snapshot
Design of experiments (DoE) in pharmaceutical development
inferredcites →https://doi.org/10.1088/2632-2153/acb416openalex-snapshot
Recipes for when physics fails: recovering robust learning of physics informed neural networks
inferredcites →https://doi.org/10.1089/gen.39.06.18openalex-snapshot
The Move toward Biopharma 4.0
inferredcites →https://doi.org/10.1098/rsta.2020.0093openalex-snapshot
Physics-informed machine learning: case studies for weather and climate modelling
inferredcites →https://doi.org/10.3389/fceng.2023.1157889openalex-snapshot
Hybrid modeling for biopharmaceutical processes: advantages, opportunities, and implementation
inferredcites →https://doi.org/10.3390/bioengineering4010021openalex-snapshot
Hybrid Approach to State Estimation for Bioprocess Control
inferredcites →https://doi.org/10.3390/cells11121929openalex-snapshot
Metabolic Profiling of CHO Cells during the Production of Biotherapeutics
inferredcites →https://doi.org/10.3390/pr7030166openalex-snapshot
Accelerating Biologics Manufacturing by Upstream Process Modelling
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1801.06637openalex-snapshot
Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/bit.70339openalex-snapshot
Hybrid Dynamic Modelling With Gaussian Process Regression for Intensified Fed‐Batch CHO Cell Culture Processes
inferred← citeshttps://doi.org/10.1007/s00033-026-02735-7openalex-snapshot
A machine learning approach to colon cancer modeling of intestinal epithelial cells using physics-informed neural networks
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2025.109354openalex-snapshot
A Physics-Informed Neural Network (PINN) framework for generic bioreactor modelling
inferred← citeshttps://doi.org/10.1016/j.csbj.2024.10.016openalex-snapshot
Predictive dynamic control accurately maps the design space for 2,3-butanediol production
inferred← citeshttps://doi.org/10.1016/j.gerr.2026.100217openalex-snapshot
Physics-Informed Neural Network Framework for Performance Prediction and Parameter Identification of Microbial Fuel Cells
inferred← citeshttps://doi.org/10.1016/j.jpha.2026.101777openalex-snapshot
Agentic Spatial Digital Twins in Pharmacology: Decoding 4D Drug Response Dynamics within the Tissue Microenvironment
inferred← citeshttps://doi.org/10.1016/j.jprocont.2026.103776openalex-snapshot
Physics-informed neural network modeling of purple non-sulfur bacteria growth and pH dynamics under varying light conditions
inferred← citeshttps://doi.org/10.1016/j.tifs.2026.105805openalex-snapshot
Machine learning and deep learning for flavor prediction and modeling in fermented vegetables: from multi-omics integration to digital twins
inferred← citeshttps://doi.org/10.1016/j.xphs.2025.104015openalex-snapshot
Modeling and predicting tablet dissolution slowdown using an acceleration factor approach and constrained neural network
inferred← citeshttps://doi.org/10.1021/bk-2026-1529.ch007openalex-snapshot
Strategic Integration of Mechanistic, Hybrid, and AI Models in Biopharmaceutical Manufacturing
inferred← citeshttps://doi.org/10.1021/bk-2026-1529.ch008openalex-snapshot
From Predictions to Understanding: Explainable Machine Learning in Pharmaceutical CMC Development
inferred← citeshttps://doi.org/10.2139/ssrn.5254834openalex-snapshot
A Physics-Informed Neural Network (Pinn) Framework for Generic Bioreactor Modelling
inferred← citeshttps://doi.org/10.2139/ssrn.7576401openalex-snapshot
Attention-Based LSTM for Kinetic Mechanism Identification and Parameter Estimation: The Case for Complex Bioprocesses
inferred← citeshttps://doi.org/10.2139/ssrn.7592691openalex-snapshot
Mechanistic Modelling and Physics-Informed Recurrent Neural Network Emulation of a Two-Stage Anaerobic Digestion System
inferred← citeshttps://doi.org/10.3390/foods15152627openalex-snapshot
Physics-Guided Residual Learning with Conditional Modulation for Quality Monitoring of Kiwifruit Juice During Pasteurization
inferred← citeshttps://doi.org/10.51847/qvvvq8ooygopenalex-snapshot
AI-Guided Bioprocess Control Should Optimize Critical Quality Attributes Before Productivity
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.