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Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions. paper by Independent / Community
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Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions.

by Independent / Community 00264722dab10b6e0eee0715cbb41cd35b5b007a
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66.0
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A: Authority 77
P: Popularity 52
R: Recency 100
Q: Quality 65
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Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the ...

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Semantic Scholar 12 Citations
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Registry ID 00264722dab10b6e0eee0715cbb41cd35b5b007a
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BibTeX 
@misc{00264722dab10b6e0eee0715cbb41cd35b5b007a,
  author = {Unknown},
  title = {Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions. Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00264722dab10b6e0eee0715cbb41cd35b5b007a}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions. [Paper]. Free2AITools. https://api.semanticscholar.org/00264722dab10b6e0eee0715cbb41cd35b5b007a

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Semantic (S) 50

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Authority (A) 77
Popularity (P) 52
Recency (R) 100
Quality (Q) 65

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FNI V2.0 for Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions.: Authority (A:77), Popularity (P:52), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"Emerging concepts from scientific deep machine learning such as physics-informed neural networks (PINNs) enable a data-driven approach for the study of complex kinetic problems. We present an extended framework that combines the advantages of PINNs with the detailed consideration of experimental parameter variations for the simulation and prediction of chemical reaction kinetics. The approach is based on truncated Taylor series expansions for the underlying fundamental equations, whereby the ..."

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@article{Unknown2026Scientific,
  title={Scientific Deep Machine Learning Concepts for the Prediction of Concentration Profiles and Chemical Reaction Kinetics: Consideration of Reaction Conditions.},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

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πŸ“ˆ12CitationsSemantic Scholar
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