Solving problems of analytic continuation with ML

Solving problems of analytic continuation with ML

An EPFL student has shown how deep learning can be used to analytically connect digital simulations and experimental results more quickly and reliably than conventional methods. This work, which the student carried out for his semester project, was recently published in Physical Review Letters.

It’s not unusual for scientists to compare experimental results with the predictions made by theoretical models. And nowadays, generating these predictions usually involves digital methods. For physicists, these methods sometimes require them to work in what they call imaginary time, where their results must be translated before they can be compared to laboratory data. This translation process is referred to as analytic continuation.

As part of his semester project, which he did in the lab of EPFL’s Chair of Computational Condensed Matter Physics (C3MP), Romain Fournier applied machine learning to the problem of analytic continuation. His findings were just published in the leading physics journal Physical Review Letters – which is highly unusual for an undergraduate project.

Prior knowledge

“The main challenge in the translation process is that there’s an unlimited number of mathematical solutions to a given problem,” says Fournier, who recently began his doctoral studies in statistics at the University of Oxford. “It’s a little like, instead of being asked what 2+2 equals, you were asked what math operation gives an answer of 4.

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Source: “Solving problems of analytic continuation through machine learning”, Willam Tuerler, EPFL News