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Smart Modeling: On the Convergence of Scientific and Engineering Models

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Various disciplines use models for different purposes. Engineers, e.g., software engineers, use engineering models to represent the system to implement, and scientists, e.g., environmentalists, use scientific models to represent the complexity of the world to understand and reason over it for analysis purpose. While the former tries to integrate all the properties in between the various engineering involved in the development process, the latter use models to internalize all the possible externalities of any changes, and later perform trade-off analysis.

With the advent of smart CPS, the combination of scientific and engineering models becomes essential, respectively for openly and freely involving massive open data and predictive models in the decision process (either for trade-off analysis or dynamic adaptation purposes), and engineering models to support the smart design and reconfiguration process of modern CPS. It urges to provide the relevant facilities to software engineers for integrating into the future CPS the various models existing from the scientific community, and thus to support informed decisions, a broader engagement of the various stakeholders (incl. scientists, decision makers and the general public), and dynamic adaptations with regards to the expected political impact of the smart CPS.

To motivate this challenge, I present various application domains where the combination of the two kinds of models is more than expected. Then I highlight some important differences in the underlying foundations that currently prevent their possible combination in a given development project.

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Smart Modeling: On the Convergence of Scientific and Engineering Models

  1. 1. CHALLENGE @ AMM, SAN VIGILIO DI MAREBBE, IT. SMART MODELING On the Convergence of Scientific and Engineering Models BENOIT COMBEMALE PROFESSOR IN SOFTWARE ENGINEERING UNIV. TOULOUSE, FRANCE HTTP://COMBEMALE.FR BENOIT.COMBEMALE@IRIT.FR @BCOMBEMALE
  2. 2. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 From Software Systems ▸ software design models for functional and non-functional properties Engineers System Models Software
  3. 3. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 To Cyber-Physical Systems ▸ multi-engineering design models for global system properties ▸ models @ runtime (i.e., included into the control loop) for dynamic adaptations Engineers System Models Cyber-Physical System sensors actuators Physical System Software <<controls>><<senses>>
  4. 4. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 To Cyber-Physical Systems ▸ multi-engineering design models for global system properties ▸ models @ runtime (i.e., included into the control loop) for dynamic adaptations Engineers System Models Cyber-Physical System sensors actuators Physical System Software <<controls>><<senses>>
  5. 5. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 To Smart Cyber-Physical Systems ▸ analysis models (incl. large-scale simulation, constraint solver) of the surrounding context related to global phenomena (e.g. physical, economical, and social laws) ▸ predictive models (predictive techniques from AI, machine learning, SBSE, fuzzy logic) ▸ user models (incl., general public/community preferences) and regulations (political laws) Engineers System Models Smart Cyber-Physical System Context sensors actuators Physical System Software <<controls>><<senses>>
  6. 6. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 And… What about Scientific Modeling? ▸ Models (computational and data-intensive sciences) for analyzing and understanding physical phenomena Context Heuristics-Laws Scientists Physical Laws (economic, environmental, social)
  7. 7. Simulator Context Simulation Processes Heuristics-Laws Scientists Physical Laws (economic, environmental, social) <<represents>> Smart Modeling Benoit Combemale @ WMM, Jan. 2018 And… What about Scientific Modeling? ▸ Simulators for tradeoff analysis, what-if scenarios, analysis of alternatives and adaptations to environmental changes, etc.
  8. 8. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 Smart Modeling: A Grand Challenge ▸ Convergence of engineering and scientific models ▸ Engineering requires scientific models ▸ Science requires engineering models ▸ Grand Challenge: a modeling framework to support the integration of data from sensors, open data, laws, regulations, scientific models (computational and data-intensive sciences), engineering models and preferences. ▸ Domain-specific languages (DSLs) for socio-technical coordination ▸ to engage engineers, scientists, decision makers, communities and the general public ▸ to integrate analysis/predictive/user models into the control loop of smart CPS
  9. 9. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 Smart Modeling: A Grand Challenge ▸ Foundational ▸ Alignment and/or combination of different foundational, native, concepts ▸ Scale change, uncertainty, conflicting views are of primary importance in scientific models ▸ Diversity/complexity of DSL relationships ▸ Far beyond structural/behavioral alignment, refinement, decomposition ▸ Separation of concerns vs. Zoom-in/Zoom-out ▸ Data-intensive vs. Computational Models ▸ Integration of analysis and predictive models into DSL (syntax and semantics) ▸ Technological ▸ Live and collaborative (meta)modeling ▸ Minimize the round trip between the DSL specification, the model, and its application (interpretation/compilation) ▸ Model experiencing environments (MEEs): what-if/for scenarios, trade-off analysis, design-space exploration ▸ Web-based, cloud-based... modeling environments ▸ Social ▸ Model, model interpretation, uncertainty… rendering ▸ Social translucence
  10. 10. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 The Sustainability Evaluation ExperienceR (SEER) Sustainability System (e.g., smart farm) ( Context sensors actuators Production/ Consumption System (e.g. farm) Software <<controls>><<senses>> ▸ Smart Cyber-Physical Systems Modeling for Sustainability B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray In Modeling in Software Engineering 2016 (MiSE'16)
  11. 11. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 The Sustainability Evaluation ExperienceR (SEER) Heuristics-Laws Scientists Open Data Scientific Models / Physical Laws (economic, environmental, social) SEER Sustainability System (e.g., smart farm) ( Context sensors actuators Production/ Consumption System (e.g. farm) Software <<controls>><<senses>> <<supplement field data>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>> ▸ Based on informed decisions ▸ with environmental, social and economic laws ▸ with open data Modeling for Sustainability B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray In Modeling in Software Engineering 2016 (MiSE'16)
  12. 12. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 The Sustainability Evaluation ExperienceR (SEER) Heuristics-Laws Scientists Open Data General Public (e.g., individuals) Policy Makers (e.g., mayor) MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER Sustainability System (e.g., smart farm) ( Context sensors actuators Production/ Consumption System (e.g. farm) Software <<controls>><<senses>>Communities (e.g., farmers) <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>> ▸ Providing a broader engagement ▸ with "what-if" scenarios for general public and policy makers Modeling for Sustainability B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray In Modeling in Software Engineering 2016 (MiSE'16)
  13. 13. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 The Sustainability Evaluation ExperienceR (SEER) Heuristics-Laws Scientists Open Data General Public (e.g., individuals) Policy Makers (e.g., mayor) MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER Sustainability System (e.g., smart farm) ( Context sensors actuators Production/ Consumption System (e.g. farm) Software <<controls>><<senses>>Communities (e.g., farmers) <<adapt>> <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>> ▸ Supporting automatic adaptation ▸ for dynamically adaptable systems Modeling for Sustainability B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray In Modeling in Software Engineering 2016 (MiSE'16)
  14. 14. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 The Sustainability Evaluation ExperienceR (SEER) Heuristics-Laws Scientists Open Data General Public (e.g., individuals) Policy Makers (e.g., mayor) MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER Sustainability System (e.g., smart farm) ( Context sensors actuators Production/ Consumption System (e.g. farm) Software <<controls>><<senses>>Communities (e.g., farmers) <<adapt>> <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>> ▸ Application to health, farming system, smart grid… Modeling for Sustainability B. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray In Modeling in Software Engineering 2016 (MiSE'16)
  15. 15. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 The Sustainability Evaluation ExperienceR (SEER) Heuristics-Laws Scientists Open Data General Public (e.g., individuals) Policy Makers (e.g., mayor) MEEs ("what-if" scenarios) Scientific Models / Physical Laws (economic, environmental, social) SEER Sustainability System (e.g., smart farm) ( Context sensors actuators Production/ Consumption System (e.g. farm) Software <<controls>><<senses>>Communities (e.g., farmers) <<adapt>> <<supplement field data>> <<provide configuration, preferences, questions>> <<present possible future and variable indicators>> <<feed>> <<integrate>> <<explore model relations (tradeoff, impact and conflict)>> Farmers Agronomist Irrigation System in collaboration with MDE in Practice for Computational Science Jean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène Raynal In International Conference on Computational Science (ICCS), 2015
  16. 16. - 16 https://github.com/gemoc/farmingmodeling FARMING SYSTEM MODELING (/ SOFTWARE DEFINED FARMING) Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016
  17. 17. - 17 WATER FLOOD PREDICTION Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016
  18. 18. - 18 HIGH PERFORMANCE COMPUTING Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016
  19. 19. - 19 HIGH PERFORMANCE COMPUTING Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016
  20. 20. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 Conclusion ▸ Integration of scientific models in the control loop of smart CPS is key to provide more informed decisions, a broader engagement, and eventually relevant runtime reconfigurations ▸ SEER is a particular instantiation of such a vision for sustainability systems
  21. 21. Smart Modeling Benoit Combemale @ WMM, Jan. 2018 Take Away Messages ▸ From MDE to SLE ▸ Language workbenches support DS(M)L development ▸ On the globalization of modeling languages (GEMOC Initiative) ▸ Integrate heterogeneous models representing different engineering concerns ▸ Language interfaces to support structural and behavioral relationships between domains (i.e., DSLs) ▸ From software systems to smart CPS ▸ Interactions with the physical world limited to (i.e., fixed, in closed world) control laws and data from the sensors ▸ What about the broader context in which the system involves? ▸ Physical / social / economic laws ▸ Predictive models ▸ Regulations, user preferences
  22. 22. Smart Modeling Abstract. Various disciplines use models for different purposes. Engineers, e.g., software engineers, use engineering models to represent the system to implement, and scientists, e.g., environmentalists, use scientific models to represent the complexity of the world to understand and reason over it for analysis purpose. While the former tries to integrate all the properties in between the various engineering involved in the development process, the latter use models to internalize all the possible externalities of any changes, and later perform trade-off analysis. With the advent of smart CPS, the combination of scientific and engineering models becomes essential, respectively for openly and freely involving massive open data and predictive models in the decision process (either for trade-off analysis or dynamic adaptation purposes), and engineering models to support the smart design and reconfiguration process of modern CPS. It urges to provide the relevant facilities to software engineers for integrating into the future CPS the various models existing from the scientific community, and thus to support informed decisions, a broader engagement of the various stakeholders (incl. scientists, decision makers and the general public), and dynamic adaptations with regards to the expected political impact of the smart CPS. To motivate this challenge, I will present various application domains where the combination of the two kinds of models is more than expected. Then I will highlight some important differences in the underlying foundations that currently prevent their possible combination in a given development project. Smart Modeling Benoit Combemale @ WMM, Jan. 2018

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