Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Gilani, Faheem"'
A simple and efficient Bayesian machine learning (BML) training and forecasting algorithm, which exploits only a 20-year short observational time series and an approximate prior model, is developed to predict the Ni\~no 3 sea surface temperature (SST
Externí odkaz:
http://arxiv.org/abs/2104.01435
A nonparametric method to predict non-Markovian time series of partially observed dynamics is developed. The prediction problem we consider is a supervised learning task of finding a regression function that takes a delay embedded observable to the o
Externí odkaz:
http://arxiv.org/abs/2007.04286
A periodic orbit on a frictionless billiard table is a piecewise linear path of a billiard ball that begins and ends at the same point with the same angle of incidence. The period of a primitive periodic orbit is the number of times the ball strikes
Externí odkaz:
http://arxiv.org/abs/1911.01397
Autor:
Gilani, Faheem, Harlim, John
A mesh-free numerical method for solving linear elliptic PDE's using the local kernel theory that was developed for manifold learning is proposed. In particular, this novel approach exploits the local kernel theory which allows one to approximate the
Externí odkaz:
http://arxiv.org/abs/1809.05894
Publikováno v:
In Physica D: Nonlinear Phenomena April 2021 418
Autor:
Gilani, Faheem, Harlim, John
Publikováno v:
In Journal of Computational Physics 15 October 2019 395:563-582