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Found 4 papers, 3 papers with code
Computationally-efficient initialisation of GPs: The generalised variogram method
We present a computationally-efficient strategy to initialise the hyperparameters of a Gaussian process (GP) avoiding the computation of the likelihood function.
Towards Optimally Weighted Physics-Informed Neural Networks in Ocean Modelling
We explore the trade-offs of using data vs. physical models in PINNs for solving partial differential equations.
Gaussian process imputation of multiple financial series
In Financial Signal Processing, multiple time series such as financial indicators, stock prices and exchange rates are strongly coupled due to their dependence on the latent state of the market and therefore they are required to be jointly analysed.
MOGPTK: The Multi-Output Gaussian Process Toolkit
We present MOGPTK, a Python package for multi-channel data modelling using Gaussian processes (GP).
