Characterisation of data sets corresponding to dynamical statistical systems using machine learning
US11113627B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Dec 7, 2017 |
| Grant date | Sep 7, 2021 |
| Priority date | — |
| Expiry date | Jul 8, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2111/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Machine learning is performed on input data representing a dynamical statistical system of entities having plural primary variables that vary time. A distribution function over time of the density of entities in a phase space, whose dimensions are the primary variables and secondary variables dependent on the rate of change of the primary variables, is derived and encoded as a sum of contour functions over time describing the contour in phase space of plural phaseons which are entities of a model of the dynamical statistical system that are localised in the phase space. Machine learning is performed on the encoded distribution function and/or at least one field in the effective configuration space whose dimensions are the primary variables, derived from the encoded distribution function. The encoding of the distribution function provides a representation which improves the performance of the machine learning techniques by simplifying hyperparameter optimisation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.