NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications
The term <i>NeuralODE</i> describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/19/3202 |
| _version_ | 1797479713607778304 |
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| author | Tobias Thummerer Johannes Stoljar Lars Mikelsons |
| author_facet | Tobias Thummerer Johannes Stoljar Lars Mikelsons |
| author_sort | Tobias Thummerer |
| collection | DOAJ |
| description | The term <i>NeuralODE</i> describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU. |
| first_indexed | 2024-03-09T21:49:45Z |
| format | Article |
| id | doaj.art-52adbfa74ff44a8e81d8f470eb77c6da |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T21:49:45Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-52adbfa74ff44a8e81d8f470eb77c6da2023-11-23T20:08:05ZengMDPI AGElectronics2079-92922022-10-011119320210.3390/electronics11193202NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World ApplicationsTobias Thummerer0Johannes Stoljar1Lars Mikelsons2Chair of Mechatronics, University of Augsburg, Am Technologiezentrum 8, 86159 Augsburg, GermanyChair of Mechatronics, University of Augsburg, Am Technologiezentrum 8, 86159 Augsburg, GermanyChair of Mechatronics, University of Augsburg, Am Technologiezentrum 8, 86159 Augsburg, GermanyThe term <i>NeuralODE</i> describes the structural combination of an Artificial Neural Network (ANN) and a numerical solver for Ordinary Differential Equations (ODE), the former acts as the right-hand side of the ODE to be solved. This concept was further extended by a black-box model in the form of a Functional Mock-up Unit (FMU) to obtain a subclass of NeuralODEs, named NeuralFMUs. The resulting structure features the advantages of the first-principle and data-driven modeling approaches in one single simulation model: a higher prediction accuracy compared to conventional First-Principle Models (FPMs) and also a lower training effort compared to purely data-driven models. We present an intuitive workflow to set up and use NeuralFMUs, enabling the encapsulation and reuse of existing conventional models exported from common modeling tools. Moreover, we exemplify this concept by deploying a NeuralFMU for a consumption simulation based on a Vehicle Longitudinal Dynamics Model (VLDM), which is a typical use case in the automotive industry. Related challenges that are often neglected in scientific use cases, such as real measurements (e.g., noise), an unknown system state or high-frequency discontinuities, are handled in this contribution. To build a hybrid model with a higher prediction quality than the original FPM, we briefly highlight two open-source libraries: FMI.jl, which allows for the import of FMUs into the Julia programming language, as well as the library FMIFlux.jl, which enables the integration of FMUs into neural network topologies to obtain a NeuralFMU.https://www.mdpi.com/2079-9292/11/19/3202NeuralFMUFMUfunctional mock-up unitNeuralODEhybrid modelFMI |
| spellingShingle | Tobias Thummerer Johannes Stoljar Lars Mikelsons NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications Electronics NeuralFMU FMU functional mock-up unit NeuralODE hybrid model FMI |
| title | NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications |
| title_full | NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications |
| title_fullStr | NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications |
| title_full_unstemmed | NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications |
| title_short | NeuralFMU: Presenting a Workflow for Integrating Hybrid NeuralODEs into Real-World Applications |
| title_sort | neuralfmu presenting a workflow for integrating hybrid neuralodes into real world applications |
| topic | NeuralFMU FMU functional mock-up unit NeuralODE hybrid model FMI |
| url | https://www.mdpi.com/2079-9292/11/19/3202 |
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