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pro vyhledávání: '"George, P. K."'
Various controllability conditions have been obtained by researchers for heterogeneous networked systems with linear dynamics. However, the literature for nonlinear, heterogeneous networked systems is comparatively less. In this paper we analyse the
Externí odkaz:
http://arxiv.org/abs/2412.12135
In this paper we extend the work in the conference paper 'On the Controllability and Observability of Heterogeneous Networked Systems with distinct node dimensions and inner-coupling matrices' wherein the controllability and observability of a hetero
Externí odkaz:
http://arxiv.org/abs/2410.13251
Autor:
Jha, Dev Prakash, George, Raju K.
This paper investigates the existence and uniqueness of the mild solutions and the exact null controllability for a class of non-autonomous parabolic evolution systems with nonlocal conditions in Hilbert spaces. We present sufficient conditions for a
Externí odkaz:
http://arxiv.org/abs/2409.16087
Autor:
Jha, Dev Prakash, George, Raju K
In this paper, we investigate the controllability of systems characterized by conformable fractional-order derivatives. We begin by establishing the existence and uniqueness of the evolution operator for a class of non-autonomous fractional-order hom
Externí odkaz:
http://arxiv.org/abs/2408.13814
A series of reduced-order numerical simulations on a specific bluff body type (v-gutters) in a subsonic duct flow is done to assess the unsteady wake dynamics. Two of the v-gutter's geometrical parameters are varied: the v-gutter's base angle ($\thet
Externí odkaz:
http://arxiv.org/abs/2204.11938
Autor:
George, Jonathan K., Solyanik-Gorgone, Maria, Yang, Hangbo, Wong, Chee Wei, Sorger, Volker J.
Convolutions are one of the most relevant operations in artificial intelligence (AI) systems. High computational complexity scaling poses significant challenges, especially in fast-responding network-edge AI applications. Fortunately, the convolution
Externí odkaz:
http://arxiv.org/abs/2202.06444
We introduce a novel training procedure for policy gradient methods wherein episodic memory is used to optimize the hyperparameters of reinforcement learning algorithms on-the-fly. Unlike other hyperparameter searches, we formulate hyperparameter sch
Externí odkaz:
http://arxiv.org/abs/2112.01853
Autor:
Amin, Rubab, George, Jonathan K., Wang, Hao, Maiti, Rishi, Ma, Zhizhen, Dalir, Hamed, Khurgin, Jacob B., Sorger, Volker J.
The high demand for machine intelligence of doubling every three months is driving novel hardware solutions beyond charging of electrical wires given a resurrection to application specific integrated circuit (ASIC)-based accelerators. These innovatio
Externí odkaz:
http://arxiv.org/abs/2109.09440
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Autor:
Yu, Ting, Ma, Xiaoxuan, Pastor, Ernest, George, Jonathan K., Wall, Simon, Miscuglio, Mario, Simpson, Robert E., Sorger, Volker J.
Deeplearning algorithms are revolutionising many aspects of modern life. Typically, they are implemented in CMOS-based hardware with severely limited memory access times and inefficient data-routing. All-optical neural networks without any electro-op
Externí odkaz:
http://arxiv.org/abs/2102.10398