Kinetics-Constrained Neural Ordinary Differential Equations: Artificial Neural Network Models tailored for Small Data to boost Kinetic Model Development Article Swipe
Aleksandr Fedorov
,
Anna Perechodjuk
,
David Linke
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-x39xt
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2023-x39xt
Artificial neural networks (ANNs) are powerful tools for solving a wide range of tasks in fundamental and applied science. However, training and building reliable ANN models requires a lot of data which so far hinders their wider application in kinetic modelling where typically only small (experimental) datasets are available. In the present work we propose a method to design ANN models for kinetic modelling that can be trained even with small data sets as are typically available. The key idea is to constrain the architecture of the ANN models by integrating kinetic and thermodynamic knowledge leading to what we call Kinetics-Constrained Neural Ordinary Differential Equations (KCNODE). The feasibility and effectiveness of the approach is first demonstrated in a numerical experiment using the catalytic hydrogenation of CO2 to methane as example. Next, we demonstrate the approach for real experimental data of a more complex reaction, the hydrogenation of CO2 to higher hydrocarbons (CO2-FT). Finally, the ANN trained for CO2-FT is used to derive an improved mechanistic model for the reverse water gas shift reaction which is a key reaction in the CO2-FT reaction network. This last step exemplifies how the opportunity to obtain reliable ANN models from small data opens new ways to approach kinetic model development.
Related Topics
Concepts
Artificial neural network
Ordinary differential equation
Computer science
Key (lock)
Range (aeronautics)
Experimental data
Kinetic energy
Small data
Artificial intelligence
Machine learning
Differential equation
Mathematics
Engineering
Physics
Mathematical analysis
Statistics
Quantum mechanics
Aerospace engineering
Computer security
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2023-x39xt
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/645cb08ffb40f6b3ee651520/original/kinetics-constrained-neural-ordinary-differential-equations-artificial-neural-network-models-tailored-for-small-data-to-boost-kinetic-model-development.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376569828
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4376569828Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2023-x39xtDigital Object Identifier
- Title
-
Kinetics-Constrained Neural Ordinary Differential Equations: Artificial Neural Network Models tailored for Small Data to boost Kinetic Model DevelopmentWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-05-15Full publication date if available
- Authors
-
Aleksandr Fedorov, Anna Perechodjuk, David LinkeList of authors in order
- Landing page
-
https://doi.org/10.26434/chemrxiv-2023-x39xtPublisher landing page
- PDF URL
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/645cb08ffb40f6b3ee651520/original/kinetics-constrained-neural-ordinary-differential-equations-artificial-neural-network-models-tailored-for-small-data-to-boost-kinetic-model-development.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/645cb08ffb40f6b3ee651520/original/kinetics-constrained-neural-ordinary-differential-equations-artificial-neural-network-models-tailored-for-small-data-to-boost-kinetic-model-development.pdfDirect OA link when available
- Concepts
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Artificial neural network, Ordinary differential equation, Computer science, Key (lock), Range (aeronautics), Experimental data, Kinetic energy, Small data, Artificial intelligence, Machine learning, Differential equation, Mathematics, Engineering, Physics, Mathematical analysis, Statistics, Quantum mechanics, Aerospace engineering, Computer securityTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 2Per-year citation counts (last 5 years)
- References (count)
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60Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4221012132, https://openalex.org/W2952655500, https://openalex.org/W3167842959, https://openalex.org/W3163993681, https://openalex.org/W2899283552, https://openalex.org/W3093036756, https://openalex.org/W3139532774, https://openalex.org/W2766447205, https://openalex.org/W2592684016, https://openalex.org/W4317952015, https://openalex.org/W4318071628, https://openalex.org/W2137983211, https://openalex.org/W1986760892, https://openalex.org/W2622826443, https://openalex.org/W3101294892, https://openalex.org/W3152893301, https://openalex.org/W2944851425, https://openalex.org/W3197694049, https://openalex.org/W4226064683, https://openalex.org/W4297826903, https://openalex.org/W3201374602, https://openalex.org/W3204384749, https://openalex.org/W3007609684, https://openalex.org/W4226320333, https://openalex.org/W2997479617, https://openalex.org/W2051959027, https://openalex.org/W3022155618, https://openalex.org/W3200208184, https://openalex.org/W2557163785, https://openalex.org/W2154046554, https://openalex.org/W2765906321, https://openalex.org/W4321792736, https://openalex.org/W3116487054, https://openalex.org/W2611869399, https://openalex.org/W3134468376, https://openalex.org/W3034197674, https://openalex.org/W2580790804, https://openalex.org/W2315929257, https://openalex.org/W2054149256, https://openalex.org/W2133379226, https://openalex.org/W2020773365, https://openalex.org/W4297780228, https://openalex.org/W2964121744, https://openalex.org/W3099878876, https://openalex.org/W3103145119, https://openalex.org/W6634682946, https://openalex.org/W2010682399, https://openalex.org/W3208469308, https://openalex.org/W4220991695, https://openalex.org/W4281683410, https://openalex.org/W2007622953, https://openalex.org/W4295312788, https://openalex.org/W633020765, https://openalex.org/W4281845262, https://openalex.org/W3151107810, https://openalex.org/W2997591727, https://openalex.org/W2953641512, https://openalex.org/W2342249984, https://openalex.org/W2242464395, https://openalex.org/W3003257820 |
| referenced_works_count | 60 |
| abstract_inverted_index.a | 9, 27, 55, 117, 140, 175 |
| abstract_inverted_index.In | 49 |
| abstract_inverted_index.an | 162 |
| abstract_inverted_index.as | 73, 128 |
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| abstract_inverted_index.is | 80, 113, 158, 174 |
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| abstract_inverted_index.ANN | 24, 59, 87, 154, 193 |
| abstract_inverted_index.CO2 | 125, 147 |
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| abstract_inverted_index.and | 16, 21, 92, 108 |
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| abstract_inverted_index.more | 141 |
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| abstract_inverted_index.step | 185 |
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| abstract_inverted_index.Next, | 130 |
| abstract_inverted_index.first | 114 |
| abstract_inverted_index.model | 165, 204 |
| abstract_inverted_index.opens | 198 |
| abstract_inverted_index.range | 11 |
| abstract_inverted_index.shift | 171 |
| abstract_inverted_index.small | 44, 70, 196 |
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| abstract_inverted_index.their | 35 |
| abstract_inverted_index.tools | 6 |
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| abstract_inverted_index.water | 169 |
| abstract_inverted_index.where | 41 |
| abstract_inverted_index.which | 31, 173 |
| abstract_inverted_index.wider | 36 |
| abstract_inverted_index.(ANNs) | 3 |
| abstract_inverted_index.CO2-FT | 157, 180 |
| abstract_inverted_index.Neural | 101 |
| abstract_inverted_index.derive | 161 |
| abstract_inverted_index.design | 58 |
| abstract_inverted_index.higher | 149 |
| abstract_inverted_index.method | 56 |
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| abstract_inverted_index.obtain | 191 |
| abstract_inverted_index.applied | 17 |
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| abstract_inverted_index.methane | 127 |
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| abstract_inverted_index.reverse | 168 |
| abstract_inverted_index.solving | 8 |
| abstract_inverted_index.trained | 67, 155 |
| abstract_inverted_index.Finally, | 152 |
| abstract_inverted_index.However, | 19 |
| abstract_inverted_index.Ordinary | 102 |
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| abstract_inverted_index.building | 22 |
| abstract_inverted_index.datasets | 46 |
| abstract_inverted_index.example. | 129 |
| abstract_inverted_index.improved | 163 |
| abstract_inverted_index.network. | 182 |
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| abstract_inverted_index.(CO2-FT). | 151 |
| abstract_inverted_index.(KCNODE). | 105 |
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| abstract_inverted_index.constrain | 82 |
| abstract_inverted_index.knowledge | 94 |
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