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Physics informed deep learning

PGR-P-1133

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Key facts

Type of research degree
PhD
Application deadline
Ongoing deadline
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
<h2 class="heading hide-accessible">Summary</h2>

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. <br /> <br /> 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.<br /> <br /> 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.<br />

<h2 class="heading hide-accessible">Full description</h2>

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

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>. Please state clearly in the Planned Course of Study section that you are applying for <em><strong>PHD Civil Engineering FT</strong></em> and in the research information section&nbsp;that the research degree you wish to be considered for is <em><strong>Physics informed deep learning</strong></em>&nbsp;as well as&nbsp;<a href="https://eps.leeds.ac.uk/civil-engineering/staff/756/dr-xiaohui-chen">Dr Xiaohui Chen</a> as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p>&nbsp;</p>

<h2 class="heading heading--sm">Entry requirements</h2>

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.

<h2 class="heading heading--sm">English language requirements</h2>

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.

<h2 class="heading">Funding on offer</h2>

<p><strong>Self-Funded or externally sponsored students are welcome to apply.</strong></p> <p><strong>UK&nbsp;</strong>&ndash;&nbsp;The&nbsp;<a href="https://phd.leeds.ac.uk/funding/209-leeds-doctoral-scholarships-2022">Leeds Doctoral Scholarships</a>, <a href="https://phd.leeds.ac.uk/funding/198-akroyd-and-brown-scholarship-2022">Akroyd &amp; Brown</a>, <a href="https://phd.leeds.ac.uk/funding/199-frank-parkinson-scholarship-2022">Frank Parkinson</a> and <a href="https://phd.leeds.ac.uk/funding/204-boothman-reynolds-and-smithells-scholarship-2022">Boothman, Reynolds &amp; Smithells</a> Scholarships are available to UK applicants. &nbsp;<a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.&nbsp;</p> <p><strong>Non-UK</strong>&nbsp;&ndash;The&nbsp;<a href="https://phd.leeds.ac.uk/funding/48-china-scholarship-council-university-of-leeds-scholarships-2021">China Scholarship Council - University of Leeds Scholarship</a>&nbsp;is available to nationals of China. The&nbsp;<a href="https://phd.leeds.ac.uk/funding/73-leeds-marshall-scholarship">Leeds Marshall Scholarship</a>&nbsp;is available to support US citizens. &nbsp;<a href="https://phd.leeds.ac.uk/funding/60-alumni-bursary">Alumni Bursary</a> is available to graduates of the University of Leeds.</p> <p>Please refer to the <a href="https://www.ukcisa.org.uk/">UKCISA</a> website for information regarding Fee Status for Non-UK Nationals starting from September/October 2021.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding the project, please contact Dr Xiaohui Chen<br /> e:&nbsp;&nbsp;<a href="mailto:X.Chen@leeds.ac.uk">X.Chen@leeds.ac.uk</a></p> <p>For further information regarding your application, please contact Doctoral College Admissions<br /> e:&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a></p>