Unsupervised Deep Learning Can Identify Physical Parameters
Parametrizing a problem is one of the biggest obstacles toward the application of physical methods to novel systems. In this research, we show that deep learning methods can be used to accelerate this parametrization process. We use an unsupervised learning algorithm, a variational autoencoder, to extract meaningful physical parameters from unlabeled time-series generated from classical physical systems. For example, given time series data of projectile motion and the instruction to represent the data in a small number of unspecified parameters, the model can discover, without further direction, the concepts of velocity and gravity. Using this method, one can uncover information about the physics of a system, even when the relevant physical parameters are unknown to the experimenter. This has obvious applications to novel systems in which parameters are not known a priori. Moreover, errors in the reconstruction of the parameters can be used to reveal meaningful info rmation about state transitions, which can be conceptualized as a generalization of similar unsupervised learning methods used to detect phase transitions in the thermodynamics of particle systems to systems that evolve in the time domain. While our proof-of-concept applications are modest, this method shows potential for making progress in a wide array of problems both in physics and outside of the field.