Nilam N. Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva, Nils Thuerey
Abstract Modeling complex dynamical systems with only partial knowledge of their physical mechanisms is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accurately simulate the evolution of the system dynamics over a sufficiently long time and in a physically consistent manner. Therefore, we propose a hybrid approach that uses a neural network model in combination with an incomplete partial differential equations (PDEs) solver that provides known, but incomplete physical information. In this study, we demonstrate that the results obtained from the incomplete PDEs can be efficiently corrected at every time step by the proposed hybrid neural network—PDE solver model, so that the effect of the unknown physics present in the system is correctly accounted for. For validation purposes, the obtained simulations of the hybrid model are successfully compared against results coming from the complete set of PDEs describing the full physics of the considered system. We demonstrate the validity of the proposed approach on a reactive flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Experiments are made on planar and Bunsen-type flames at various operating conditions. The hybrid neural network—PDE approach correctly models the flame evolution of the cases under study for significantly long time windows, yields improved generalization and allows for larger simulation time steps.
{"title":"Incomplete to complete multiphysics forecasting: a hybrid approach for learning unknown phenomena","authors":"Nilam N. Tathawadekar, Nguyen Anh Khoa Doan, Camilo F. Silva, Nils Thuerey","doi":"10.1017/dce.2023.20","DOIUrl":"https://doi.org/10.1017/dce.2023.20","url":null,"abstract":"Abstract Modeling complex dynamical systems with only partial knowledge of their physical mechanisms is a crucial problem across all scientific and engineering disciplines. Purely data-driven approaches, which only make use of an artificial neural network and data, often fail to accurately simulate the evolution of the system dynamics over a sufficiently long time and in a physically consistent manner. Therefore, we propose a hybrid approach that uses a neural network model in combination with an incomplete partial differential equations (PDEs) solver that provides known, but incomplete physical information. In this study, we demonstrate that the results obtained from the incomplete PDEs can be efficiently corrected at every time step by the proposed hybrid neural network—PDE solver model, so that the effect of the unknown physics present in the system is correctly accounted for. For validation purposes, the obtained simulations of the hybrid model are successfully compared against results coming from the complete set of PDEs describing the full physics of the considered system. We demonstrate the validity of the proposed approach on a reactive flow, an archetypal multi-physics system that combines fluid mechanics and chemistry, the latter being the physics considered unknown. Experiments are made on planar and Bunsen-type flames at various operating conditions. The hybrid neural network—PDE approach correctly models the flame evolution of the cases under study for significantly long time windows, yields improved generalization and allows for larger simulation time steps.","PeriodicalId":158708,"journal":{"name":"Data-Centric Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134981455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Keyao Li, Mark A. Griffin, Tamryn Barker, Zane Prickett, Melinda R. Hodkiewicz, Jess Kozman, Peta Chirgwin
Abstract There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarity, skills, and cross-team collaboration that are essential for developing organizational capabilities in data science. In this study, we introduce a data workflow method (DWM) as a new approach to break organizational silos and create a multi-disciplinary team to develop, implement and embed data science. Different from current data science process workflows, the DWM is managed at the system level that shapes business operating model for continuous improvement, rather than as a function of a particular project, one single business unit, or isolated individuals. To further operationalize the DWM approach, we investigated an embedded data workflow at a mining operation that has been using geological data in a machine-learning model to stabilize daily mill production for the last 2 years. Based on the findings in this study, we propose that the DWM approach derives its capability from three aspects: (a) a systemic data workflow; (b) multi-disciplinary networks of collaboration and responsibility; and (c) clearly identified data roles and the associated skills and expertise. This study suggests a whole-of-organization approach and pathway to develop data science capability.
{"title":"Embedding data science innovations in organizations: a new workflow approach","authors":"Keyao Li, Mark A. Griffin, Tamryn Barker, Zane Prickett, Melinda R. Hodkiewicz, Jess Kozman, Peta Chirgwin","doi":"10.1017/dce.2023.22","DOIUrl":"https://doi.org/10.1017/dce.2023.22","url":null,"abstract":"Abstract There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarity, skills, and cross-team collaboration that are essential for developing organizational capabilities in data science. In this study, we introduce a data workflow method (DWM) as a new approach to break organizational silos and create a multi-disciplinary team to develop, implement and embed data science. Different from current data science process workflows, the DWM is managed at the system level that shapes business operating model for continuous improvement, rather than as a function of a particular project, one single business unit, or isolated individuals. To further operationalize the DWM approach, we investigated an embedded data workflow at a mining operation that has been using geological data in a machine-learning model to stabilize daily mill production for the last 2 years. Based on the findings in this study, we propose that the DWM approach derives its capability from three aspects: (a) a systemic data workflow; (b) multi-disciplinary networks of collaboration and responsibility; and (c) clearly identified data roles and the associated skills and expertise. This study suggests a whole-of-organization approach and pathway to develop data science capability.","PeriodicalId":158708,"journal":{"name":"Data-Centric Engineering","volume":"364 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134981456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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