Autonomous Cooking with Digital Twin Methodology
| Autor: | Maximilian Kannapinn, Michael Schäfer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Computational Engineering
Finance and Science (cs.CE) FOS: Computer and information sciences Computer Science - Machine Learning Biological Physics (physics.bio-ph) Fluid Dynamics (physics.flu-dyn) FOS: Mathematics FOS: Physical sciences Dynamical Systems (math.DS) Physics - Biological Physics Physics - Fluid Dynamics Mathematics - Dynamical Systems Computer Science - Computational Engineering Finance and Science Machine Learning (cs.LG) |
| Popis: | This work introduces the concept of an autonomous cooking process based on Digital Twin method- ology. It proposes a hybrid approach of physics-based full order simulations followed by a data-driven system identification process with low errors. It makes faster-than-real-time simulations of Digital Twins feasible on a device level, without the need for cloud or high-performance computing. The concept is universally applicable to various physical processes. Accepted version of manuscript published in Proceedings of WCCM-ECCOMAS 2020 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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