Abstract: A system configured to determine candidate sets of values for enforceable parameters for a physical system, and for each candidate set of values, determine a performance measurement for the physical system and generate a data point having an input portion indicative of the candidate set of values and an output portion indicative of the determined performance measurement. The system is further arranged to augment each data point to include an additional dimension comprising a bias value; project each augmented data point onto a surface of a unit hypersphere of the first number of dimensions; determine, using the projected augmented data points, a set of parameter values for a sparse variational Gaussian process, GP, on said unit hypersphere; and determine, using the sparse variational GP with the determined set of parameter values, a further set of values for the set of enforceable parameters.

Abstract: A computer-implemented method of processing input data comprising a plurality of samples arranged on a regular grid within a finite sampling window, to train parameters for a kernel of a Gaussian process for modelling the data. The parameters are associated with a mixture of spectral components representing a spectral density of the kernel. The method includes: initialising the parameters; determining a cut-off frequency for delimiting a low-frequency range and a high-frequency range, the cut-off frequency being an integer multiple of a fundamental frequency corresponding to a reciprocal size of the sampling window; performing a discrete Fourier transform on the input data to determine frequency domain data; and processing a portion of the frequency domain data within the low-frequency range to determine smoothed input data.

Abstract: A system configured to determine candidate sets of values for enforceable parameters for a physical system, and for each candidate set of values, determine a performance measurement for the physical system and generate a data point having an input portion indicative of the candidate set of values and an output portion indicative of the determined performance measurement. The system is further arranged to augment each data point to include an additional dimension comprising a bias value; project each augmented data point onto a surface of a unit hypersphere of the first number of dimensions; determine, using the projected augmented data points, a set of parameter values for a sparse variational Gaussian process, GP, on said unit hypersphere; and determine, using the sparse variational GP with the determined set of parameter values, a further set of values for the set of enforceable parameters.

Abstract: A system configured to determine candidate sets of values for enforceable parameters for a physical system, and for each candidate set of values, determine a performance measurement for the physical system and generate a data point having an input portion indicative of the candidate set of values and an output portion indicative of the determined performance measurement. The system is further arranged to augment each data point to include an additional dimension comprising a bias value; project each augmented data point onto a surface of a unit hypersphere of the first number of dimensions; determine, using the projected augmented data points, a set of parameter values for a sparse variational Gaussian process, GP, on said unit hypersphere; and determine, using the sparse variational GP with the determined set of parameter values, a further set of values for the set of enforceable parameters.