Smart Modeling: On the Convergence of Scientific and Engineering Models

CHALLENGE @ AMM, SAN VIGILIO DI MAREBBE, IT.

SMART MODELINGOn the Convergence of Scientific and Engineering Models

BENOIT COMBEMALEPROFESSOR IN SOFTWARE ENGINEERINGUNIV. TOULOUSE, FRANCE

HTTP://COMBEMALE.FR [email protected]@BCOMBEMALE

Smart ModelingBenoit Combemale @ WMM, Jan. 2018

From Software Systems

▸ software design models for functionaland non-functional properties

Engineers

System Models

Software


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-PhysicalSystem

sensors actuators

Physical System

Software

<<controls>><<senses>>


To Smart Cyber-Physical Systems

▸ analysis models (incl. large-scale simulation, constraint solver) of the surrounding contextrelated to global phenomena (e.g. physical, economical, and social laws)

▸ predictive models (predictive techniques fromAI, machine learning, SBSE, fuzzy logic)

▸ user models (incl., general public/communitypreferences) and regulations (political laws)

Engineers

System Models SmartCyber-Physical System

Context

sensors actuators

Physical System

Software



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)

Simulator

Context

SimulationProcesses

Heuristics-Laws

Scientists

Physical Laws (economic, environmental, social)

<<represents>>


And… What about Scientific Modeling?

▸ Simulators for tradeoff analysis, what-if scenarios, analysis of alternatives and adaptations to environmental changes, etc.


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


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


The Sustainability Evaluation ExperienceR (SEER)

Sustainability System(e.g., smart farm)

(

Context

sensors actuators

Production/Consumption

System (e.g. farm)

Software


▸ Smart Cyber-Physical Systems

Modeling for SustainabilityB. Combemale, B. H.C. Cheng, A. Moreira, J.-M. Bruel, J. Gray

In Modeling in Software Engineering 2016 (MiSE'16)



Heuristics-Laws

Scientists

Open DataScientific Models / Physical Laws (economic, environmental, social)

SEER


(

Context

sensors actuators


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





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


(

Context

sensors actuators


System (e.g. farm)

Software

<<controls>><<senses>>Communities(e.g., farmers)

<<supplementfield data>>

<<provide configuration, preferences, questions>>

<<present possible future and variable indicators>>

<<feed>>

<<integrate>>



▸ Providing a broader engagement▸ with "what-if" scenarios for general public and policy makers





Heuristics-Laws

Scientists

Open Data


(e.g., mayor)



SEER


(

Context

sensors actuators


System (e.g. farm)

Software


<<adapt>>




<<feed>>

<<integrate>>



▸ Supporting automatic adaptation▸ for dynamically adaptable systems





Heuristics-Laws

Scientists

Open Data


(e.g., mayor)



SEER


(

Context

sensors actuators


System (e.g. farm)

Software


<<adapt>>




<<feed>>

<<integrate>>



▸ Application to health, farming system, smart grid…





Heuristics-Laws

Scientists

Open Data


(e.g., mayor)



SEER


(

Context

sensors actuators


System (e.g. farm)

Software


<<adapt>>




<<feed>>

<<integrate>>



Farmers

Agronomist

IrrigationSystem

in collaboration with

MDE in Practice for Computational ScienceJean-Michel Bruel, Benoit Combemale, Ileana Ober, Hélène RaynalIn International Conference on Computational Science (ICCS), 2015

- 16https://github.com/gemoc/farmingmodeling

FARMING SYSTEM MODELING (/ SOFTWARE DEFINED FARMING)

Modeling for Sustainability – B. Combemale (Univ. Rennes 1) – December 4th, 2016

- 17

WATER FLOOD PREDICTION


- 18

HIGH PERFORMANCE COMPUTING


- 19

HIGH PERFORMANCE COMPUTING



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 runtimereconfigurations

▸ SEER is a particular instantiation of such a vision for sustainability systems


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

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 tounderstand and reason over it for analysis purpose. While the former tries to integrate allthe 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 laterperform trade-off analysis.

With the advent of smart CPS, the combination of scientific and engineering modelsbecomes essential, respectively for openly and freely involving massive open data andpredictive models in the decision process (either for trade-off analysis or dynamicadaptation purposes), and engineering models to support the smart design andreconfiguration process of modern CPS. It urges to provide the relevant facilities to softwareengineers for integrating into the future CPS the various models existing from the scientificcommunity, and thus to support informed decisions, a broader engagement of the variousstakeholders (incl. scientists, decision makers and the general public), and dynamicadaptations with regards to the expected political impact of the smart CPS.

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


Smart Modeling: On the Convergence of Scientific and Engineering Models

Software

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