Results 161 to 170 of about 5,402 (301)

Forward uncertainty quantification in random differential equation systems with delta‐impulsive terms: Theoretical study and applications

open access: yesMathematical Methods in the Applied Sciences, Volume 48, Issue 7, Page 7609-7629, 15 May 2025.
This contribution aims at studying a general class of random differential equations with Dirac‐delta impulse terms at a finite number of time instants. Our approach directly addresses calculating the so‐called first probability density function, from which all the relevant statistical information about the solution, a stochastic process, can be ...
Vicente J. Bevia   +2 more
wiley   +1 more source

Learning with Algebraic Invariances, and the Invariant Kernel Trick

open access: green, 2014
Franz J. Király   +2 more
openalex   +2 more sources

Ruijsenaars wavefunctions as modular group matrix coefficients. [PDF]

open access: yesLett Math Phys
Di Francesco P   +4 more
europepmc   +1 more source

Flexible development and evaluation of machine‐learning‐supported optimal control and estimation methods via HILO‐MPC

open access: yesInternational Journal of Robust and Nonlinear Control, Volume 35, Issue 7, Page 2835-2859, 10 May 2025.
Abstract Model‐based optimization approaches for monitoring and control, such as model predictive control and optimal state and parameter estimation, have been used successfully for decades in many engineering applications. Models describing the dynamics, constraints, and desired performance criteria are fundamental to model‐based approaches. Thanks to
Johannes Pohlodek   +4 more
wiley   +1 more source

Randomly pivoted Cholesky: Practical approximation of a kernel matrix with few entry evaluations

open access: yesCommunications on Pure and Applied Mathematics, Volume 78, Issue 5, Page 995-1041, May 2025.
Abstract The randomly pivoted Cholesky algorithm (RPCholesky) computes a factorized rank‐k$k$ approximation of an N×N$N \times N$ positive‐semidefinite (psd) matrix. RPCholesky requires only (k+1)N$(k + 1)N$ entry evaluations and O(k2N)$\mathcal {O}(k^2 N)$ additional arithmetic operations, and it can be implemented with just a few lines of code.
Yifan Chen   +3 more
wiley   +1 more source

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