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pro vyhledávání: '"Popov, Andrey A"'
Autor:
Popov, Andrey Anatoliyevich
There once was a dream that data-driven models would replace their theory-guided counterparts. We have awoken from this dream. We now know that data cannot replace theory. Data-driven models still have their advantages, mainly in computational effici
Externí odkaz:
http://hdl.handle.net/10919/111608
Autor:
Popov, Andrey A., Zanetti, Renato
In the high-dimensional setting, Gaussian mixture kernel density estimates become increasingly suboptimal. In this work we aim to show that it is practical to instead use the optimal multivariate Epanechnikov kernel. We make use of this optimal Epane
Externí odkaz:
http://arxiv.org/abs/2408.11164
This work focuses on the critical aspect of accurate weight computation during the measurement incorporation phase of Gaussian mixture filters. The proposed novel approach computes weights by linearizing the measurement model about each component's p
Externí odkaz:
http://arxiv.org/abs/2405.11081
Data assimilation aims to estimate the states of a dynamical system by optimally combining sparse and noisy observations of the physical system with uncertain forecasts produced by a computational model. The states of many dynamical systems of intere
Externí odkaz:
http://arxiv.org/abs/2405.04380
The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical Adam algorithm is a first-
Externí odkaz:
http://arxiv.org/abs/2403.13704
Spacecraft entering Mars require precise navigation algorithms capable of accurately estimating the vehicle's position and velocity in dynamic and uncertain atmospheric environments. Discrepancies between the true Martian atmospheric density and the
Externí odkaz:
http://arxiv.org/abs/2401.14411
Few real-world systems are amenable to truly Bayesian filtering; nonlinearities and non-Gaussian noises can wreak havoc on filters that rely on linearization and Gaussian uncertainty approximations. This article presents the Bayesian Recursive Update
Externí odkaz:
http://arxiv.org/abs/2310.18442
The ensemble Gaussian mixture filter (EnGMF) is a non-linear filter suited to data assimilation of highly non-Gaussian and non-linear models that has practical utility in the case of a small number of samples, and theoretical convergence to full Baye
Externí odkaz:
http://arxiv.org/abs/2308.14143
Autor:
Minkin, Alexander S., Lebedeva, Irina V., Popov, Andrey M., Vyrko, Sergey A., Poklonski, Nikolai A., Lozovik, Yurii E.
Publikováno v:
Phys. Rev. B 108 (2023) 085411(1-9)
The potential energy surface (PES) of interlayer interaction of infinite twisted bilayer graphene is calculated for a set of commensurate moir\'e patterns using the registry-dependent Kolmogorov-Crespi empirical potential. The calculated PESs have th
Externí odkaz:
http://arxiv.org/abs/2308.06302
Autor:
Popov, Andrey A., Zanetti, Renato
Data-driven reduced order modeling of chaotic dynamics can result in systems that either dissipate or diverge catastrophically. Leveraging non-linear dimensionality reduction of autoencoders and the freedom of non-linear operator inference with neura
Externí odkaz:
http://arxiv.org/abs/2305.08036