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Results for “nonstationary”
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Flexible nonstationary spatiotemporal modeling of high-frequency monitoring data
Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of half-spectral covariance functions to the case of nonstationarity in space and time and demonstrate that this method provides a natural and tractable way to incorporate complex behaviors into a covariance model. Further, we use this method with fully time-domain computations to obtain bona fide maximum likelihood estimators—as opposed to using Whittle-type likelihood approximations, for example—that can still be computed conveniently. Additionally, we apply this method to very high-frequency Doppler LIDAR vertical wind velocity measurements, demonstrating that the model can expressively capture the extreme nonstationarity of dynamics above and below the atmospheric boundary layer and, more importantly, the interaction of the process dynamics across it.
Nonstationary multivariate Gaussian processes for electronic health records
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FreeFlux: A Python Package for Time-Efficient Isotopically Nonstationary Metabolic Flux Analysis
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Bayesian inference for high-dimensional nonstationary Gaussian processes
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Improving Probabilistic Error Cancellation in the Presence of Nonstationary Noise
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Advanced stationary and nonstationary kernel designs for domain-aware Gaussian processes
Gaussian process regression is a widely-applied method for function approximation and uncertainty quantification. The technique has gained popularity recently in the machine learning community due to its robustness and interpretability. The mathematical methods we discuss in this paper are an extension of the Gaussian-process framework. We are proposing advanced kernel designs that only allow for functions with certain desirable characteristics to be elements of the reproducing kernel Hilbert space (RKHS) that underlies all kernel methods and serves as the sample space for Gaussian process regression. These desirable characteristics reflect the underlying physics; two obvious examples are symmetry and periodicity constraints. In addition, non-stationary kernel designs can be defined in the same framework to yield flexible multi-task Gaussian processes. We will show the impact of advanced kernel designs on Gaussian processes using several synthetic and two scientific data sets. The results of our research show that including domain knowledge, communicated through advanced kernel designs, has a significant impact on the accuracy and relevance of the function approximation.
Group classification of equations describing the one dimensional nonstationary gas flows <gruppovaya klassifikatsiya uravneniy opisyva- yushchikh odnomernyye nestatsionarnyye techeniya gaza<
Group classification of equations describing one-dimensional gas flows
Investigations of formation and removal of hydrogen and oxygen depositions on platinum by a new nonstationary method <untersuchung des auf- und abbaues von wasserstoff- und sauerstoffbelegungen an platin mit einer neuen instationaren methode<
Electrode-kinetic investigation of formation and removal of hydrogen and oxygen depositions on platinum
Spectral analysis of nonstationary spacecraft vibration data
Procedures for describing and analyzing frequency composition of spacecraft launch vibration data
A nonstationary temperature problem relative to a cylindrical shell stiffened by a ring
State of stress and strain in thin walled stiffened cylindrical shells
Nonstationary radiation field
Radiation diffusion without redistribution by frequencies, equations for radiative transfer, and stellar spectrum
Calculation of the onset time of equilibrium dissociation on the basis of experimental data concerning the nonstationary propagation of a reflected shock wave
Dissociation relaxation times of carbon dioxide
Mean-square response of simple mechanical systems to nonstationary random excitation.
Mechanical single degree of freedom system mean square response to AM random noise, considering frequency response, input excitation spectrum, etc
Peak structural response to nonstationary random excitations
Study establishes distribution function of peak response values, based on frequency interpretation. Excitations considered include impact loading on landing gears and aircraft gust loading. Because of relative severity of excitations, prediction of fatigue and maximum response characteristics is important part of task of structural analysis and design.
Optimization theory for time-variant missile systems and nonstationary inputs
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Retrieval of dispersive and convective transport phenomena in fluids using stationary and nonstationary time domain analysis
Simultaneously occuring dispersive and convective components of fluid kinematics are obtained by a time domain analysis of optically retrieved temporal histories of the transport phenomena. Utilizing triangulation of collimated optical fields of view from two radiometers to obtain the temporal histories of the intensity fluctuations associated with the transport phenomena has enabled investigators to retrieve the local convective transport by employing correlation statistics. The location of the peak in the covariance curve determines the transit time from which the convection velocity is calculated; whereas, the change in shape of the peak in the covariance curve determines the change in average frequency of the wave packet from which the dispersion velocity is calculated. Thus, two-component analysis requires the maximum possible enhancement of the delineation for the transport. The convection velocity is the result of a fixed reference frame calculation whereas, the dispersion velocity is the result of a moving reference frame calcuation.
Method for analyses of cyclic physiological data that are nonstationary in time.
The proposed method of studying biologic rhythms permits the biologist to view physiological data dynamically without assuming that the data are stationary in time. Vector representation of data points is employed, and the summation of the vectors (train of vectors) produces a summation dial that is able to detect dynamic changes in the time of the peak as well as random walks (arrhythmia).