Artificial intelligence

Artificial intelligence technologies such as natural language processing (NLP) and machine learning (ML) can be used to automatically extract requirements from textual sources such as documentation, user reviews and social media. This can significantly reduce the time and effort required to manually identify and analyze requirements, and improve the accuracy and completeness of the collected requirements.

Artificial intelligence techniques such as generative-adversarial networks (GANs) and reinforcement learning (RL) can be used to automatically create system models based on high-level specifications. This can reduce the time and effort required to manually create models, and increase the level of abstraction and versatility of the models created.

Artificial intelligence technologies, such as rule-based expert systems and machine learning algorithms, can be used to automatically validate models against requirements and identify potential problems or anomalies. This can reduce the time and effort required to manually perform validation tasks and improve the accuracy and reliability of the resulting validation results.

Artificial intelligence techniques such as deep reinforcement learning (DRL) can be used to train autonomous agents to make decisions in complex and uncertain environments. This can be particularly useful in safety-critical systems where timely and accurate decision-making is necessary to prevent hazards and accidents.

AI can be used to improve human decision-making and increase the overall efficiency of the MBSE process. For example, machine learning algorithms can be used to analyse large amounts of data providing information that can be used to optimise design process.AI can also help automate certain aspects of the MBSE process, such as requirements management and validation. For example, Natural Language Processing (NLP) techniques can be used to extract requirements from text and then automatically map them into models. 

Another area where AI can be applied in MBSE is in simulation modelling. Machine learning algorithms can be used to automatically create and optimise system models based on input data such as performance metrics, environmental conditions and user feedback. This can help reduce the time and resources required to develop and test system models, while improving the accuracy and reliability of the results.

Python: The main programming language for developing AI solutions; TensorFlow and Keras: Libraries for creating and training neural networks; PyTorch: A framework for machine learning, especially effective in computer vision and natural language processing; Scikit-learn: A library for classical machine learning and data analysis; Pandas: A tool for processing and analysing structured data; NumPy: A library for working with multidimensional arrays and mathematical functions; AWS (Amazon Web Services): Cloud platform for deploying and scaling AI solutions; Microsoft Azure: Cloud services for application development, testing and management; Yandex Cloud: Russian cloud platform for data storage and computing; Autodesk Revit: Building Information Modelling (BIM) software; Dynamo: Visual programming environment for Revit that extends BIM capabilities.