Abstract: This disclosure relates to improved techniques for determining reference points for computerized simulations of physical systems and/or physical models that may be used in machine learning development architectures. This disclosure also relates to systems, methods, apparatuses, and computer program products that are configured to determine reference points for one or more parameters of a model of a physical system used in a computerized simulation of the model. The reference points may be representative of the system outputs across the parameter space, and can be determined in an efficient and computationally-feasible manner. The outputs of the computerized simulations of physical systems may then be further used to create, build, or train one or more learning models pertaining to physical systems.

Abstract: A dynamical physical system of particles represented by input data representing the phase space state of the particles over time is characterised by deriving a feature vector in respect of each particle comprising plural metrics that each describe a change in the phase space state of the particle over time. A classification of particles into plural classes is performed by applying a machine learning technique that operates on the feature vectors of the particles, and outputting classification data representing the classification.

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.

Abstract: A dynamical physical system of particles represented by input data representing the phase space state of the particles over time is characterised by deriving a feature vector in respect of each particle comprising plural metrics that each describe a change in the phase space state of the particle over time. A classification of particles into plural classes is performed by applying a machine learning technique that operates on the feature vectors of the particles, and outputting classification data representing the classification.

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.