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pro vyhledávání: '"Tronarp, Filip"'
Autor:
Tronarp, Filip
In this article, square-root formulations of the statistical linear regression filter and smoother are developed. Crucially, the method uses QR decompositions rather than Cholesky downdates. This makes the method inherently more numerically robust th
Externí odkaz:
http://arxiv.org/abs/2406.05188
Autor:
Lahr, Amon, Tronarp, Filip, Bosch, Nathanael, Schmidt, Jonathan, Hennig, Philipp, Zeilinger, Melanie N.
Appropriate time discretization is crucial for real-time applications of numerical optimal control, such as nonlinear model predictive control. However, if the discretization error strongly depends on the applied control input, meeting accuracy and s
Externí odkaz:
http://arxiv.org/abs/2401.17731
Autor:
Stillfjord, Tony, Tronarp, Filip
In this article, an efficient numerical method for computing finite-horizon controllability Gramians in Cholesky-factored form is proposed. The method is applicable to general dense matrices of moderate size and produces a Cholesky factor of the Gram
Externí odkaz:
http://arxiv.org/abs/2310.13462
Autor:
Bosch, Nathanael, Corenflos, Adrien, Yaghoobi, Fatemeh, Tronarp, Filip, Hennig, Philipp, Särkkä, Simo
Publikováno v:
Journal of Machine Learning Research, 2024
Probabilistic numerical solvers for ordinary differential equations (ODEs) treat the numerical simulation of dynamical systems as problems of Bayesian state estimation. Aside from producing posterior distributions over ODE solutions and thereby quant
Externí odkaz:
http://arxiv.org/abs/2310.01145
Inference and simulation in the context of high-dimensional dynamical systems remain computationally challenging problems. Some form of dimensionality reduction is required to make the problem tractable in general. In this paper, we propose a novel a
Externí odkaz:
http://arxiv.org/abs/2306.07774
Probabilistic solvers provide a flexible and efficient framework for simulation, uncertainty quantification, and inference in dynamical systems. However, like standard solvers, they suffer performance penalties for certain stiff systems, where small
Externí odkaz:
http://arxiv.org/abs/2305.14978
Autor:
Tronarp, Filip, Karvonen, Toni
We present a general Fourier analytic technique for constructing orthonormal basis expansions of translation-invariant kernels from orthonormal bases of $\mathscr{L}_2(\mathbb{R})$. This allows us to derive explicit expansions on the real line for (i
Externí odkaz:
http://arxiv.org/abs/2206.08648
Autor:
Tronarp, Filip, Karvonen, Toni
Publikováno v:
In Journal of Approximation Theory September 2024 302
We show how probabilistic numerics can be used to convert an initial value problem into a Gauss--Markov process parametrised by the dynamics of the initial value problem. Consequently, the often difficult problem of parameter estimation in ordinary d
Externí odkaz:
http://arxiv.org/abs/2202.01287
Probabilistic numerical solvers for ordinary differential equations compute posterior distributions over the solution of an initial value problem via Bayesian inference. In this paper, we leverage their probabilistic formulation to seamlessly include
Externí odkaz:
http://arxiv.org/abs/2110.10770