Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data
Sergei Manzhos, Eita Sasaki, Manabu Ihara
Abstract
Abstract We show that Gaussian process regression (GPR) allows representing multivariate functions with low-dimensional terms via kernel design. When using a kernel built with high-dimensional model representation (HDMR), one obtains a similar type of representation as the previously proposed HDMR-GPR scheme while being faster and simpler to use. We tested the approach on cases where highly accurate machine learning is required from sparse data by fitting potential energy surfaces and kinetic energy densities.
Topics & Concepts
Multivariate statisticsGaussian processKrigingKernel (algebra)Kinetic energyRepresentation (politics)Energy (signal processing)Process (computing)Computer scienceRegressionArtificial intelligenceGaussianKernel methodMachine learningMathematicsPattern recognition (psychology)Biological systemStatisticsSupport vector machinePhysicsChemistryComputational chemistryCombinatoricsPolitical scienceBiologyPoliticsOperating systemQuantum mechanicsLawMachine Learning in Materials ScienceGaussian Processes and Bayesian InferenceMass Spectrometry Techniques and Applications