Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg-Marquardt method for solving inverse modeling problems w...

Nonlinear inverse dynamic models can be utilized in various parts of advanced model-based control system design: reference trajectory optimization, feedforward control and feedback linearization [35]. In this paper, a new synthesis approach for nonlinear inverse dynamic models of satellites with flexible structures is presented. For satellite configurations with unstable zero dynamics, a stable...

It is well known that the parabolic partial differential equations in two or more space dimensions with overspecified boundary data, feature in the mathematical modeling of many phenomena. In this article, an inverse problem of determining an unknown time-dependent source term of a parabolic equation in general dimensions is considered. Employing some transformations, we change the inverse prob...

In large-scale energy market models, the price–demand relationship is usually represented by a linear function. this paper, nonlinear demand functions are fitted to electricity bid data; in particular, exponential and polynomial (cubic) estimated from EPEX day-ahead data (i.e. Central Western European area). The corresponding game-theoretic, models were successfully solved using Extended Mathem...

Procedural modeling is now the de facto standard of material in industry. models can be edited and are easily extended, unlike pixel-based representations captured materials. In this paper, we present a semi-automatic pipeline for general proceduralization. Given Spatially-Varying Bidirectional Reflectance Distribution Functions (SVBRDFs) represented as sets pixel maps, our decomposes them into...