Machine learning in physics
In this context, for example, it can be used as a tool to interpolate pre-calculated interatomic potentials,[10] or directly solving the Schrödinger equation with a variational method.[11] The ability to experimentally control and prepare increasingly complex quantum systems brings with it a growing need to turn large and noisy data sets into meaningful information.[35] A deep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an unpublished approach inspired by studies of visual cognition in infants.[41] Beyond discovery and prediction, "blank slate"-type of learning of fundamental aspects of the physical world may have further applications such as improving adaptive and broad artificial general intelligence.[additional citation(s) needed] In specific, prior machine learning models were "highly specialised and lack a general understanding of the world".
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