The Monte Carlo (MC) method exhibits generality and insensitivity to the number of stochastic variables, but it is expensive for accurate Average Quality Index (AQI) or Parametric Yield estimation of MOS VLSI circuits or discrete component circuits. In this paper a variant of the Latin Hypercube Sampling MC method is presented which is an efficient variance reduction technique in MC estimation....

This study represents simulation of single spot and multi spot welded specimen by using Monte carol method i.e. Latin hypercube sampling. A three dimensional nonlinear finite element analysis of multi-spot welded lap shear specimen is performed and uncertainty in shear strength of spot weld is analyzed. Thickness and nugget radius are randomly varied within effective range and their effect on t...

Robust design optimization is a modeling methodology, combined with a suite of computational tools, which is aimed to solve problems where some kind of uncertainty occurs in the data or in the model. This paper explores robust optimization complexity in the multiobjective case, describing a new approach by means of Polynomial Chaos expansions (PCE). The aim of this paper is to demonstrate that ...

This paper reports some initial investigations of the use of antithetic variates in perfect sampling. A simple random walk example is presented to illustrate the key ingredients of antithetic coupling for perfect sampling as well as its potential benefit. A key step in implementing antithetic coupling is to generate random variates that are negatively associated, a stronger condition than negat...

Model Predictive Control (MPC) is a computationally demanding control technique that allows dealing with multiple-input and multiple-output systems, while handling constraints in a systematic way. The necessity of solving an optimization problem at every sampling instant often (i) limits the application scope to slow dynamical systems and/or (ii) results in expensive computational hardware impl...

ALAMO is a computational methodology for leaning algebraic functions from data. Given a data set, the approach begins by building a low-complexity, linear model composed of explicit non-linear transformations of the independent variables. Linear combinations of these non-linear transformations allow a linear model to better approximate complex behavior observed in real processes. The model is r...

We develop efficient Monte Carlo methods for estimating the failure probability of a system. An example of the problem comes from an approach for probabilistic safety assessment of nuclear power plants known as riskinformed safety-margin characterization, but it also arises in other contexts, e.g., structural reliability, catastrophe modeling, and finance. We estimate the failure probability us...

Metamodels are effective for providing fast-running surrogate approximations of product or system performance. Since these approximations are generally based on continuous functions, they can provide poor fits of discontinuous response functions. Many engineering models produce functions that are only piecewise continuous, due to changes in modes of behavior or other state variables. In this pa...

We introduce a new class of designs, called marginally coupled designs, for computer experiments with both qualitative and quantitative variables. The proposed design maintains an economic run size with attractive space-filling properties. The design points for quantitative factors forms a Latin hypercube design. In addition, for each level of any qualitative factor of a marginally coupled desi...

We study k dimensional Latin hypercubes of order n. We describe the automorphism groups of the hypercubes and define the parity of a hypercube and relate the parity with the determinant of a permutation hypercube. We determine the parity in the orbits of the automorphism group. Based on this definition of parity we make a conjecture similar to the Alon-Tarsi conjecture. We define an orthogonali...