Physics informed deep learning

Key facts

Artificial Intelligence has attracted considerable attention from Civil Engineers over the last decade, especially Artificial Neural Networks (ANN) which can provide a flexible mathematical structure capable of identifying complex nonlinear relationships between input and output data sets. However, traditionally, ANNs have been trained using input and output datasets with simple loss functions, which incorporates no physical system knowledge into the learning process, while requiring huge amounts of data, which are either costly to produce or unavailable.

Alternatively, conceptual and computational modelling has continued rapid development in the past decades as it is based on fundamental constitutive physical equations and able to provide accurate analysis and prediction, however, it suffers from problems associated with computational performance, which can hinder its usage.

This project will integrate fundamental physics into the training process of deep learning processes, engaging computational modelling and deep neural network, tested by real experiments/modelling to establish a new generation of physics informed deep learning methods, which can provide accurate and quick estimates of geotechnical and geoenvironmental systems response.

The outcome of this work will highly benefit Geotechnical & Environmental engineering in climate-changing scenarios, for instance in the forecasting of highly nonlinear flooding/drought influence on the resilience of the geosystem.

To apply for this project you will need to make a formal application for research degree study through the University website. You will need to create a login ID with a username and PIN.

• For Application type please select Research Degrees – Research Postgraduate.

• The admission year for this project is 2027/28 Academic Year.

• You will need to select your Planned Course of Study from a drop-down menu. For this project, scroll down and select PHP Civil Engineering Full Time.

• The project start date for this project is 1 October 2027, please use this as your Proposed Start Date of Research.

• Please state clearly in the Research Information Section that the research degree you wish to be considered for is Physics informed deep learning as well as Dr Xiaohui Chen as your proposed supervisor.

Please state clearly in the Finance section, the funding that you are applying for, if you are self-funding or externally sponsored.

More information on how to apply is available on our website here. You will be required to provide a personal statement which outlines your interest in the project you are applying for, why you have chosen it and how your skills map onto the requirements of the project.

We will assess applications continuously as we receive them. We welcome and strongly encourage any potential applicants to contact the supervisor(s) for an informal discussion, prior to applying, and recommend submitting your application early.

If you are applying with external sponsorship or you are funding your own study, please ensure you provide your supporting documents at the point you submit your application:

Full Transcripts of all degree study or if in final year of study, full transcripts to date including the grading scheme

Personal Statement outlining your interest in the project

If English is not your first language, you must provide evidence that you meet the University's minimum English language requirements (below).

As an international research-intensive university, we welcome students from all walks of life and from across the world. We foster an inclusive environment where all can flourish and prosper, and we are proud of our strong commitment to student education. Across all Faculties we are dedicated to diversifying our community and we welcome the unique contributions that individuals can bring, and particularly encourage applications from, but not limited to Black, Asian, people who belong to a minority ethnic community, people who identify as LGBT+ and people with disabilities. Applicants will always be selected based on merit and ability.

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid. Some schools and faculties have a higher requirement.

This is a non funded PhD project. Applicants are expected to be self funded or to secure external funding.

You can find out more about our Funding Opportunities on our Postgraduate Research Opportunities website.

Important: Please note that all costs associated with your arrival at Leeds (visa, Immigration Health Surcharge, flights etc) would have to be met by yourself, or you will need to find an alternative funding source.

For further information about this project, please contact Dr Xiaohui Chen by email to X.Chen@leeds.ac.uk

For further information about your application, including how to apply, please contact PGR Admissions by email to epspgradmissions@leeds.ac.uk

• Type of research degree: PhD
• Application deadline: Monday 31 May 2027
• Project start date: Friday 1 October 2027
• Country eligibility: International (open to all nationalities, including the UK)
• Funding: Non-funded
• Supervisors: Dr Xiaohui Chen
• Schools: School of Civil Engineering
• Research groups/institutes: Water, Public Health and Environmental Engineering

Research areas