相关论文: A Gaussian process model for stellar activity in 2…
Spatio-temporal processes in environmental applications are often assumed to follow a Gaussian model, possibly after some transformation. However, heterogeneity in space and time might have a pattern that will not be accommodated by…
Decoders built on Gaussian processes (GPs) are enticing due to the marginalisation over the non-linear function space. Such models (also known as GP-LVMs) are often expensive and notoriously difficult to train in practice, but can be scaled…
Learning time-series models is useful for many applications, such as simulation and forecasting. In this study, we consider the problem of actively learning time-series models while taking given safety constraints into account. For…
One of the focus areas of modern scientific research is to reveal mysteries related to genes and their interactions. The dynamic interactions between genes can be encoded into a gene regulatory network (GRN), which can be used to gain…
Context. Magnetic fields exhibit a wide variety of behaviours in low mass stars and further characterization is required to understand these observations. Stellar photometry from space missions such as MOST, CoRoT, Kepler, and, in future…
Learning-based approaches are increasingly leveraged to manage and coordinate the operation of grid-edge resources in active power distribution networks. Among these, model-based techniques stand out for their superior data efficiency and…
The study of stellar activity cycles is crucial to understand the underlying dynamo and how it causes activity signatures such as dark spots and bright faculae. We study the appearance of activity signatures in contemporaneous photometric…
The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeling, and analyzing real-world measurements of nonlinear…
We present K2SC (K2 Systematics Correction), a Python pipeline to model instrumental systematics and astrophysical variability in light curves from the K2 mission. K2SC uses Gaussian process regression to model position-dependent…
Mostly multiband photometric transit observations have been used so far to retrieve broadband transmission spectra of transiting exoplanets in order to study their atmosphere. An alternative method has been proposed and has only been used…
Young and active stars generally have regular, almost sinusoidal, patterns of variability attributed to their rotation, while the majority of older and less active stars, including the Sun, have more complex and non-regular light-curves…
Over the past decade, a number of algorithms for full-field elastic strain estimation from neutron and X-ray measurements have been published. Many of the recently published algorithms rely on modelling the unknown strain field as a…
This paper presents an approach for constrained Gaussian Process (GP) regression where we assume that a set of linear transformations of the process are bounded. It is motivated by machine learning applications for high-consequence…
The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution of some real-world phenomenon over time, for example models…
Magnetic fields and stellar spots can alter the equivalent widths of absorption lines in stellar spectra, varying during the activity cycle. This also influences the information that we derive through spectroscopic analysis. In this study…
Accurately modeling effects from stellar activity is a key step in detecting radial velocity signals of low-mass and long-period exoplanets. Radial velocities from stellar activity are dominated by magnetic active regions that move in and…
Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning, but that are rarely used in signal processing. In this tutorial, we present GPs for regression as…
With advances in scientific computing and mathematical modeling, complex scientific phenomena such as galaxy formations and rocket propulsion can now be reliably simulated. Such simulations can however be very time-intensive, requiring…
The intrinsic variability due to the magnetic activity of young active stars is one of the main challenges in detecting and characterising exoplanets. We present a method able to model the stellar photosphere and its surface inhomogeneities…
Discrete automated processes in industrial and cyber-physical systems often exhibit a repetitive structure in which successive repetitions follow a common trajectory while differing in duration, amplitude, and fine-scale dynamics. Such…