Conciliating accuracy and efficiency to empower engineering based on performance: a short journey
This paper revisits the different arts of engineering. The art of modeling for describing the behavior of complex systems from the solution of partial differential equations that are expected to govern their responses. Then, the art of simulation concerns the ability of solving these complex mathema...
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| Format: | Article |
| Language: | English |
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Académie des sciences
2023-06-01
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| Series: | Comptes Rendus. Mécanique |
| Subjects: | |
| Online Access: | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.188/ |
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| author | Chinesta, Francisco Cueto, Elias |
| author_facet | Chinesta, Francisco Cueto, Elias |
| author_sort | Chinesta, Francisco |
| collection | DOAJ |
| description | This paper revisits the different arts of engineering. The art of modeling for describing the behavior of complex systems from the solution of partial differential equations that are expected to govern their responses. Then, the art of simulation concerns the ability of solving these complex mathematical objects expected to describe the physical reality as accurately as possible (accuracy with respect to the exact solution of the models) and as fast as possible. Finally, the art of decision making needs to ensure accurate and fast predictions for efficient diagnosis and prognosis. For that purpose physics-informed digital twins (also known as Hybrid Twins) will be employed, allying real-time physics (where complex models are solved by using advanced model order reduction techniques) and physics-informed data-driven models for filling the gap between the reality and the physics-based model predictions. The use of physics-aware data-driven models in tandem with physics-based reduced order models allows us to predict very fast without compromising accuracy. This is compulsory for diagnosis and prognosis purposes. |
| format | Article |
| id | doaj-art-d071ac20ee2447b4aa9342c3b19d4fa8 |
| institution | Kabale University |
| issn | 1873-7234 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Académie des sciences |
| record_format | Article |
| series | Comptes Rendus. Mécanique |
| spelling | doaj-art-d071ac20ee2447b4aa9342c3b19d4fa82025-02-07T13:47:59ZengAcadémie des sciencesComptes Rendus. Mécanique1873-72342023-06-01351S312113310.5802/crmeca.18810.5802/crmeca.188Conciliating accuracy and efficiency to empower engineering based on performance: a short journeyChinesta, Francisco0Cueto, Elias1CNRS@CREATE LTD, 1 Create Way, 08-01 CREATE Tower, Singapore 138602; PIMM lab, Arts et Metiers Institute of Technology, 151 Boulevard de Hôpital, 75013 Paris, FranceAragon Institute of Engineering Research, Universidad de Zaragoza, Maria de Luna s/n, 50018 Zaragoza, SpainThis paper revisits the different arts of engineering. The art of modeling for describing the behavior of complex systems from the solution of partial differential equations that are expected to govern their responses. Then, the art of simulation concerns the ability of solving these complex mathematical objects expected to describe the physical reality as accurately as possible (accuracy with respect to the exact solution of the models) and as fast as possible. Finally, the art of decision making needs to ensure accurate and fast predictions for efficient diagnosis and prognosis. For that purpose physics-informed digital twins (also known as Hybrid Twins) will be employed, allying real-time physics (where complex models are solved by using advanced model order reduction techniques) and physics-informed data-driven models for filling the gap between the reality and the physics-based model predictions. The use of physics-aware data-driven models in tandem with physics-based reduced order models allows us to predict very fast without compromising accuracy. This is compulsory for diagnosis and prognosis purposes.https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.188/Physics-based modelingMachine learningArtificial IntelligenceData-driven modelingModel Order ReductionPODPGDVirtualDigital and Hybrid Twins |
| spellingShingle | Chinesta, Francisco Cueto, Elias Conciliating accuracy and efficiency to empower engineering based on performance: a short journey Comptes Rendus. Mécanique Physics-based modeling Machine learning Artificial Intelligence Data-driven modeling Model Order Reduction POD PGD Virtual Digital and Hybrid Twins |
| title | Conciliating accuracy and efficiency to empower engineering based on performance: a short journey |
| title_full | Conciliating accuracy and efficiency to empower engineering based on performance: a short journey |
| title_fullStr | Conciliating accuracy and efficiency to empower engineering based on performance: a short journey |
| title_full_unstemmed | Conciliating accuracy and efficiency to empower engineering based on performance: a short journey |
| title_short | Conciliating accuracy and efficiency to empower engineering based on performance: a short journey |
| title_sort | conciliating accuracy and efficiency to empower engineering based on performance a short journey |
| topic | Physics-based modeling Machine learning Artificial Intelligence Data-driven modeling Model Order Reduction POD PGD Virtual Digital and Hybrid Twins |
| url | https://comptes-rendus.academie-sciences.fr/mecanique/articles/10.5802/crmeca.188/ |
| work_keys_str_mv | AT chinestafrancisco conciliatingaccuracyandefficiencytoempowerengineeringbasedonperformanceashortjourney AT cuetoelias conciliatingaccuracyandefficiencytoempowerengineeringbasedonperformanceashortjourney |