Integration of Mathematical Models and Simulations

Transcription

1 Integration of Mathematical Models and Simulations Systems Net Presentation 10 th February 2016 Robert Luff

2 Scope of Presentation

3 The Problem Space Existing models have different assumptions The need to connect them together for co-simulation Model A Model A Model B Integration Model B Model C

4 Model Static representation Programme for Simulation Innovation Definitions Simulation Dynamic representation Integration Where models and simulations are connected in such a way as to achieve behaviour that is not possible otherwise Co-Simulation - The successful integration of two or more simulations that share values during run time The English language is descriptive rather than prescriptive

5 Current Methods of Model Selection Functionality-based Experience and engineering judgement Tied to run time environment restrictions Companies don t always publish their methods

6 Assuring Integration of Models & Simulations Current methods Strict standards (ensures semantic similarities) Integration environments (ensures correct data handling) Confidence levels several aspects Unit testing

7 Current Methods for Model Integration Custom hack using bespoke software Strict standard with specified architecture and specified interfaces Proprietary integration environment using protected source code

8 Modelling and Simulation in the Near Future Differences: Packages/software Methods of expressing information Hardware (that it runs on) Similarities: Physical aspects of the real world Physics models Use of computers

9 Physical assumptions Programme for Simulation Innovation Other Similarities Textual requirements Textual documentation what the model is how it works what is required to get it to work

10 Not all Integration is Meaningful Models may be of physically similar components Model boundaries may be similar Names, data types etc. However there are differences such as Assumptions, Viewpoints, Accuracy, Requirements, Purpose of the Test, Captured Information

11 Use of Existing Standards The standard that is closest to covering the identified information has been found to be Functional Mockup Interface (FMI). FMI is not specifically intended for this use case, however it could be extended to cover more of the semantic content of the models and simulations.

12 Capturing Semantic Information A means of capturing the semantic information Four tables to record the information Model information Structure Environment Verification experiment One structure which captures multiple forms of model & simulation

13 Table Information Model Information Structure Model Environment Category Verification Value Experiment Category Value Category Value

14 fill out the tables? How to utilise the information? compare models or simulations that are detailed in the tables?

15 Natural Language Processing (NLP) The automated processing of natural language by a computer This is by no means as simple as it first seems It has the potential to populate the tables and analyse the similarities between the tables

16 Language: Descriptive v Prescriptive Descriptive: The definitions and grammatical syntax of words change over time Prescriptive: The definitions and grammatical syntax of words are prescribed Both forms of Language have strengths and caveats

17 Implications of Descriptive Language for NLP Inconsistences not just over time but between different viewpoints at the same time Dealing with exceptions

18 Rule-based NLP Rule-based NLP uses logical rules to conduct analyses Strengths: Quick to develop Fast execution for simple algorithms Intuitive in construction Verification can be conducted manually Caveats: There will always be exceptions that slip through the algorithms Often only works on a subset of text which follows a specific form or style

19 Sentiment-based NLP Sentiment-based NLP attempts to gain an understanding of what the sentence is trying to convey - this is what Google use for their translator service Strengths: Sentences with different words / structures but with the same meaning can be analysed and compared to show their similarities Caveats: Require large initial data sets which needs to be manually constructed Constant verification is needed and weightings need to be updated Can be much slower than rule-based analysis for simple tasks

20 Decision to Use Rule-based Access to documentation and source information for development Access to tools to develop the programs Time to develop a proof of concept to use NLP in this use case Verification is a crucial component of the process and unambiguous test cases are needed

21 NLP Program Gives similarity measure between textual documentation that will indicate the likelihood of model integration analysis of integration tables to demonstrate similarity of the models/simulations an automated way of obtaining the information that is needed for the integration tables

22 Programming Languages and Environment Ref:

23 1 st Algorithm

24 2 nd Algorithm

25 3 rd Algorithm

26 End-to-end Support for Model Integration Process flow diagram Model requirements writing guide Necessary information for integration Framework to support the capture of the necessary information Selection aid (NLP) Verification procedure

27 Future Work Development of guidance to use the end to end process Further development of NLP program Validation with Industry case study

28 Any Questions?