4,937 research outputs found
An Efficient Framework For Fast Computer Aided Design of Microwave Circuits Based on the Higher-Order 3D Finite-Element Method
In this paper, an efficient computational framework for the full-wave design by optimization of complex microwave passive devices, such as antennas, filters, and multiplexers, is described. The framework consists of a computational engine, a 3D object modeler, and a graphical user interface. The computational engine, which is based on a finite element method with curvilinear higher-order tetrahedral elements, is coupled with built-in or external gradient-based optimization procedures. For speed, a model order reduction technique is used and the gradient computation is achieved by perturbation with geometry deformation, processed on the level of the individual mesh nodes. To maximize performance, the framework is targeted to multicore CPU architectures and its extended version can also use multiple GPUs. To illustrate the accuracy and high efficiency of the framework, we provide examples of simulations of a dielectric resonator antenna and full-wave design by optimization of two diplexers involving tens of unknowns, and show that the design can be completed within the duration of a few simulations using industry-standard FEM solvers. The accuracy of the design is confirmed by measurements
Dimension reduction for Gaussian process emulation: an application to the influence of bathymetry on tsunami heights
High accuracy complex computer models, or simulators, require large resources
in time and memory to produce realistic results. Statistical emulators are
computationally cheap approximations of such simulators. They can be built to
replace simulators for various purposes, such as the propagation of
uncertainties from inputs to outputs or the calibration of some internal
parameters against observations. However, when the input space is of high
dimension, the construction of an emulator can become prohibitively expensive.
In this paper, we introduce a joint framework merging emulation with dimension
reduction in order to overcome this hurdle. The gradient-based kernel dimension
reduction technique is chosen due to its ability to drastically decrease
dimensionality with little loss in information. The Gaussian process emulation
technique is combined with this dimension reduction approach. Our proposed
approach provides an answer to the dimension reduction issue in emulation for a
wide range of simulation problems that cannot be tackled using existing
methods. The efficiency and accuracy of the proposed framework is demonstrated
theoretically, and compared with other methods on an elliptic partial
differential equation (PDE) problem. We finally present a realistic application
to tsunami modeling. The uncertainties in the bathymetry (seafloor elevation)
are modeled as high-dimensional realizations of a spatial process using a
geostatistical approach. Our dimension-reduced emulation enables us to compute
the impact of these uncertainties on resulting possible tsunami wave heights
near-shore and on-shore. We observe a significant increase in the spread of
uncertainties in the tsunami heights due to the contribution of the bathymetry
uncertainties. These results highlight the need to include the effect of
uncertainties in the bathymetry in tsunami early warnings and risk assessments.Comment: 26 pages, 8 figures, 2 table
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