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On Physics Informed Neural Networks (PINNs) for approximating PDEs

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MDLW03 - Deep learning and partial differential equations

PIN Ns have been very successfully employed in the approximation of both forward and inverse problems for a variety of PDEs. We will present error estimates for PIN Ns with the aim of explaining some aspects of their performance. Applications to both forward and inverse problems for different PDEs will be also be presented. 

This talk is part of the Isaac Newton Institute Seminar Series series.

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Online via zoom: https://cam-ac-uk.zoom.us/meeting/register/tJArd-2vqDssGN3r3r9JzlCL9w3NoHlB21an MR14 (TBC), Centre for Mathematical Sciences, Wilberforce Road, Cambridge Old Divinity School, St. John's College, Cambridge

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