Self-learning surrogate models in superstructure optimization
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Scientists in many disciplines have progressively been using simulations to better understand the natural systems they study. Faster hardware, as well as
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Understanding properly the impact of model parameters and their interactions on the predictions is required for an appropriate model assessment. For simulation of reflooding following a LOCA, this requirement is justified because the important parameters a ...
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Exploiting recent progress [1]-[4] in the characterization of the detection performance of diffusion strategies over adaptive multi-agent networks: i) we present two theoretical approximations, one based on asymptotic normality and the other based on the t ...
The Degree of Non-Linearity (DNL) of scattering problems and specifically of differential Microwave Imaging (dMWI) problems is addressed in this paper. The quantification of the DNL amounts to evaluate the validity range of the Born first order (linear) ap ...
While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) c ...
Rocket and gas turbine combustion dynamics involves a confluence of diverse physics and interaction across a number of system components. Any comprehensive, self-consistent numerical model is burdened by a very large computational mesh, stiff unsteady proc ...
American Institute of Aeronautics and Astronautics2014
Classical list scheduling is a very popular and efficient technique for scheduling jobs for parallel and distributed platforms. It is inherently centralized. However, with the increasing number of processors, the cost for managing a single centralized list ...