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Deep learning for wavefunctions (2)

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If you have a question about this talk, please contact nrc25 .

See the TCM graduate teaching page for further information.

In this series of graduate lectures we will study the application of deep neural networks to the approximation of wavefunctions. Since 2017 there has been a surge of interest in this area and this looks set to accelerate. Knowledge of quantum mechanics will be assumed up to early graduate level but familiarity with deep neural networks is not essential.

In this lecture we will discuss the application of deep learning to Fermionic systems primarily in real space. We will also discuss optimization challenges arising from using large deep neural networks in this context and how they can be mitigated.

This talk is part of the TCM Graduate Lectures series.

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