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Playing Newton: Learning equations of motion from data

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If you have a question about this talk, please contact Julius Bier Kirkegaard.

Arguably, science’ goal of understanding nature can be formulated as inferring mathematical laws that govern natural systems from experimental data. With the fast growth of power of modern computers and of artificial intelligence algorithms, there has been a recent surge in attempts to automate this goal and to design, to some extent, an “artificial scientist.” I will discuss this emerging field, but will focus primarily on our own approach to it. I will introduce an algorithm that we have recently developed, which allows one to infer the underlying dynamical equations behind a noisy time series, even if the dynamics are nonlinear, and only a few of the relevant variables are measured. I will illustrate the method on applications to toy problems, including inferring the iconic Newton’s law of universal gravitation, and dynamics of a few synthetic biochemical systems. I will end with applications to real biological data: modeling calcium dynamics in pancreatic beta cells, as well as modeling the landscape of possible behavioral states underlying reflexive escape from pain in a roundworm.

This talk is part of the DAMTP BioLunch series.

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