We introduce and study a randomized quasi-Monte Carlo method for estimating the state distribution at each step of a Markov chain. The number of steps in the chain can be random an...
In solving application problems, the data sets used to train a neural network may not be hundred percent precise but within certain ranges. Representing data sets with intervals, ...
Abstract--In this paper, we present a new approach to calculate the steady state resistance values for CMOS library gates. These resistances are defined as simple equivalent models...
We present a framework to solve a finite-time optimal control problem for parabolic partial differential equations (PDEs) with diffusivity-interior actuators, which is motivate...
We present an efficient implementation of an approximate balanced truncation model reduction technique for general large-scale RLC systems, described by a statespace model where t...