Zobrazeno 1 - 10
of 37
pro vyhledávání: '"Nabi, Saleh"'
In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, t
Externí odkaz:
http://arxiv.org/abs/2212.13559
Koopman operator theory is receiving increased attention due to its promise to linearize nonlinear dynamics. Neural networks that are developed to represent Koopman operators have shown great success thanks to their ability to approximate arbitrarily
Externí odkaz:
http://arxiv.org/abs/2211.09419
Autor:
Mowlavi, Saviz, Nabi, Saleh
Physics-informed neural networks (PINNs) have recently become a popular method for solving forward and inverse problems governed by partial differential equations (PDEs). By incorporating the residual of the PDE into the loss function of a neural net
Externí odkaz:
http://arxiv.org/abs/2111.09880
Autor:
Mowlavi, Saviz, Nabi, Saleh
Publikováno v:
In Journal of Computational Physics 15 January 2023 473
Autor:
Pan, Yangchen, Farahmand, Amir-massoud, White, Martha, Nabi, Saleh, Grover, Piyush, Nikovski, Daniel
Recent work has shown that reinforcement learning (RL) is a promising approach to control dynamical systems described by partial differential equations (PDE). This paper shows how to use RL to tackle more general PDE control problems that have contin
Externí odkaz:
http://arxiv.org/abs/1806.06931
Publikováno v:
Phys. Rev. Fluids 4, 013801 (2019)
A One-Dimensional (1D) Reduced-Order Model (ROM) has been developed for a 3D Rayleigh-B\'enard convection system in the turbulent regime with Rayleigh number $\mathrm{Ra}=10^6$. The state vector of the 1D ROM is horizontally averaged temperature. Usi
Externí odkaz:
http://arxiv.org/abs/1805.01596
Publikováno v:
IEEE Transactions on Automation Science and Engineering: A Publication of the IEEE Robotics and Automation Society; October 2024, Vol. 21 Issue: 4 p5691-5699, 9p
Publikováno v:
SIAM Journal on Applied Dynamical Systems 16(2) (2017)
We present a sparse sensing framework based on Dynamic Mode Decomposition (DMD) to identify flow regimes and bifurcations in large-scale thermo-fluid systems. Motivated by real-time sensing and control of thermal-fluid flows in buildings and equipmen
Externí odkaz:
http://arxiv.org/abs/1510.02831
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.