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Large Language Models (LLM) for Automated Finite Element Analysis

This is a Self-Funded project - please only apply if you have funding to undertake this project

Large Language Models such as Chat GPT3 represent a significant development in artificial intelligence and its impact on society. Trained on a huge amount of data and able to a generate useful responses to complex and multi-layered questions, it is potentially the most useful tool available for handling structural engineering tasks. However, it's results are not always completely accurate and sometimes completely incorrect, though delivered with authority.

This project will develop a computational interface to a leading LLM (such as the latest Chat GPT) through its API to assess the capability of the model to develop finite element models, run them and summarise results in a manner similar to a human analyst.

The research will aim to identify the following:

  1. The limits of the models understanding of structural engineering concepts through its ability to answer complex technical questions correctly.
  2. The use of the LLM for automating the development of finite element models for a range of analyses such as stress, heat and modal response. The models will be developed to solve specific questions and may include complex and nonlinear physics.
  3. Integration of methods such as Monte Carlo simulation for assessing the uncertainty in the analyses.
  4. Generation of automated technical reports that summarise the results of the analyses in a human readable format, including relevant screenshots, technical descriptions, motivation and diagrams and suggest follow on analyses where necessary. A wide range of automated tests such as unit testing, integration testing, functional testing and acceptance testing will need to be developed to determine the LLM's ability as a design assistant.

The output of the research will be a computational interface to a LLM which can operate as a structural engineering design assistant to automatically develop comprehensive finite element models that solve complex engineering challenges and summarise the outputs in the form of technical reports.

This project will require familiarity with programming languages such as Java, C++, Python or similar as well as an understanding of structural engineering design.

The successful applicant will join the Centre for AI: Social and Digital Innovation, which has more than 15 PhD researchers working in the field of computer vision, robotics, drones and includes a newly fitted artificial intelligence and robotics lab.

How to apply

If you are interested in applying for the above PhD topic please follow the steps below:

  1. Contact the supervisor by email or phone to discuss your interest and find out if you would be suitable. Supervisor details can be found on this topic page. The supervisor will guide you in developing the topic-specific research proposal, which will form part of your application.
  2. Click on the 'Apply here' button on this page and you will be taken to the relevant PhD course page, where you can apply using an online application.
  3. Complete the online application indicating your selected supervisor and include the research proposal for the topic you have selected.

Good luck!

This is a self funded topic

Brunel offers a number of funding options to research students that help cover the cost of their tuition fees, contribute to living expenses or both. See more information here: The UK Government is also offering Doctoral Student Loans for eligible students, and there is some funding available through the Research Councils. Many of our international students benefit from funding provided by their governments or employers. Brunel alumni enjoy tuition fee discounts of 15%.

Meet the Supervisor(s)

Michael Rustell - Michael is a chartered civil engineer (CEng MICE) who holds an Engineering Doctorate (EngD) in artificial intelligence in design automation of civil infrastructure. He has spent the past 8 years in industry working in ports and marine, oil & gas and nuclear industries and was a lead data scientist for the Europe, Middle East and Africa (EMEA) region at AECOM prior to joining Brunel.  Michaels interests include: deep learning, machine learning and data science in civil engineering, natural language processing, design automation and stochastic design methods.

Tatiana Kalganova -


  • PhD Napier University
  • Research-engineer degree Belarusian State University of Informatics and Radio-electronics, Minsk, Belarus
  • MSc (distinction) Belarusian State University of Informatics and Radio-electronics, Minsk, Belarus


  • 2000-present Lecturer Brunel University London
  • 2003-2011 Business Fellow London Technology Network, LTN Link between research activities at Brunel University London and industry
  • 1997-2000 PhD student Napier University
  • 1994-1997 Research Assistant Belarusian State University of Informatics and Radio-electronics