SwePub
Sök i SwePub databas

  Utökad sökning

Träfflista för sökning "WFRF:(Hennig Jörg) "

Sökning: WFRF:(Hennig Jörg)

  • Resultat 1-2 av 2
Sortera/gruppera träfflistan
   
NumreringReferensOmslagsbildHitta
1.
  • Arun, K. G., et al. (författare)
  • New horizons for fundamental physics with LISA
  • 2022
  • Ingår i: Living Reviews in Relativity. - : Springer Science and Business Media LLC. - 1433-8351 .- 2367-3613. ; 25:1
  • Forskningsöversikt (refereegranskat)abstract
    • The Laser Interferometer Space Antenna (LISA) has the potential to reveal wonders about the fundamental theory of nature at play in the extreme gravity regime, where the gravitational interaction is both strong and dynamical. In this white paper, the Fundamental Physics Working Group of the LISA Consortium summarizes the current topics in fundamental physics where LISA observations of gravitational waves can be expected to provide key input. We provide the briefest of reviews to then delineate avenues for future research directions and to discuss connections between this working group, other working groups and the consortium work package teams. These connections must be developed for LISA to live up to its science potential in these areas.
  •  
2.
  • Schmidt, Jonathan, et al. (författare)
  • The Rank-Reduced Kalman Filter : Approximate Dynamical-Low-Rank Filtering In High Dimensions
  • 2023
  • Ingår i: Advances in Neural Information Processing Systems. ; 36
  • Konferensbidrag (refereegranskat)abstract
    • 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 approximate Gaussian filtering and smoothing method which propagates low-rank approximations of the covariance matrices. This is accomplished by projecting the Lyapunov equations associated with the prediction step to a manifold of low-rank matrices, which are then solved by a recently developed, numerically stable, dynamical low-rank integrator. Meanwhile, the update steps are made tractable by noting that the covariance update only transforms the column space of the covariance matrix, which is low-rank by construction. The algorithm differentiates itself from existing ensemble-based approaches in that the low-rank approximations of the covariance matrices are deterministic, rather than stochastic. Crucially, this enables the method to reproduce the exact Kalman filter as the low-rank dimension approaches the true dimensionality of the problem. Our method reduces computational complexity from cubic (for the Kalman filter) to quadratic in the state-space size in the worst-case, and can achieve linear complexity if the state-space model satisfies certain criteria. Through a set of experiments in classical data-assimilation and spatio-temporal regression, we show that the proposed method consistently outperforms the ensemble-based methods in terms of error in the mean and covariance with respect to the exact Kalman filter. This comes at no additional cost in terms of asymptotic computational complexity.
  •  
Skapa referenser, mejla, bekava och länka
  • Resultat 1-2 av 2

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

 
pil uppåt Stäng

Kopiera och spara länken för att återkomma till aktuell vy