Physically, this amounts to approximating the input current I by a sequence of weak instantaneous charge injections, arriving at times ( j − 1 / 2 ) Δ, j = 1, 2, 3, … .
Using cuboid decomposition, an initial polycube approximating the input boundary mesh is built.
The algorithm transforms the problem of controlling non-affine systems to control of nonlinear affine systems and then, by approximating the inverse of the input function, calculates feasible control input.
Most numerical approaches address the problem either by computing local sensitivity indices (partial derivatives of the solution with respect to the input variables) [ 5, 6], by solving the ODE for a statistically large ensemble of random or quasi-random input values [ 7- 9], or by approximating the functional relationship of the input and output [ 10- 12].
In this paper, an extended MVU (EMVU) method is proposed, extending the utilization of MVU to new samples by approximating the nonlinear mapping between the input space and the output space with a Gaussian process model.
In the texture-mapping procedure, we begin by approximating the target surface with an approximating cylinder.
For example, ChangeFinder approximates the input data items' distribution through a mixture of normal distributions.
SRSM is one of the ideal conventional sampling based method for uncertainty analysis and this is accomplished by approximating both inputs and outputs of the uncertain system through stochastic series of well-behaved standard random variable (srv).
Using any of these bases to approximate the input signal generates approximation error.
Our XVA approach can be implemented by means of nested Monte Carlo simulations for approximating the loss process L required as input data in the KVA computations.
