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.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-28. (canceled) 29. A method of characterising a dynamical physical system of particles represented by input data representing the phase space state of the particles over time, the method comprising: deriving from the input data 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; and performing classification of particles into plural classes by applying a machine learning technique that operates on the feature vectors of the particles, and outputting classification data representing the classification. 30. The method of claim 29, wherein the phase space state represented by the input data includes positions of the particles. 31. The method of claim 30, wherein the phase space state represented by the input data further includes properties of the particles that are dependent on motion of the particles. 32. The method of claim 31, wherein the metrics include one or more metrics that describe motion of the particle, and include one or more of the following: a vector or magnitude of displacement from an initial position over time; a vector or magnitude of velocity over time; or a vector or magnitude of rate of displacement from an initial position over time. 33. The method of claim 32, wherein the metrics include a metric that represents a distribution of path-lengths of straight-line displacements of the particle. 34. The method of claim 32, wherein the metrics include a metric that represents the Lorentz factor of the particle over time. 35. The method of claim 29, wherein the metrics include one or more metrics that describe the position and/or time of an event related to a change in energy of the particle. 36. The method of claim 35, wherein the event is related to a discontinuous change in energy of the particle. 37. The method of claim 35, wherein the metrics include a metric that is a vector or magnitude of displacement from an initial position prior to a change in energy. 38. The method of claim 29, wherein the metrics include a representation of a trajectory of the particle. 39. The method of claim 29, wherein the metrics comprise scaled quantities. 40. The method of claim 29, wherein the classes correspond to different types of dynamical process acting in the dynamical physical system. 41. The method of claim 29, wherein the machine learning technique is a supervised machine learning technique trained using feature vectors of training examples of particles in respective classes. 42. The method of claim 41, further comprising a preliminary step of training the classification using the feature vectors of training examples of particles in the respective classes. 43. The method of claim 29, wherein the machine learning technique is an unsupervised machine learning technique that compares the feature vectors of the particles as between different particles. 44. The method of claim 43, further comprising relating the plural classes of particles to different types of dynamical process acting in the dynamical physical system. 45. The method of claim 29, wherein the input data is derived from physical measurements of a dynamical physical system which are obtained from an output of a particle-in-cell numerical simulation; wherein the input data is derived using a compressive sensing technique; and wherein the input data represents the dynamical physical system over successive intervals in time, and the method is performed on the input data in respect of each interval of time. 46. The method of claim 29, further comprising generating a graphical representation of the distribution of particles in the classes. 47. The method of claim 29, wherein the particles comprise groups of molecules, individual molecules, atoms, ions, sub-atomic particles, or any combination thereof, and wherein the particles have an average diameter of 100 μm or less. 48. A system for characterising a dynamical physical system of particles represented by input data representing the phase space state of the particles over time comprising: a computing device that includes a computer-readable storage medium storing a computer program, the computer program being configured to cause the computer apparatus to: derive from the input data 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; and perform classification of particles into plural classes by applying a machine learning technique that operates on the feature vectors of the particles, and outputting classification data representing the classification.