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Although the behavior of several RF circuit blocks can be accurately evaluated via transistor-level simulations, the design space exploration is limited by the high computational cost of such simulations. Therefore, cheap-to-evaluate surrogate models of the circuit simulator are introduced. This paper presents some results of a feasibility study concerning the development of surrogate models of low noise amplifiers. [ABSTRACT FROM AUTHOR]

Surrogate models are a cost-effective replacement for expensive computer simulations in design space exploration. Literature has already demonstrated the feasibility of accurate surrogate models for single radio frequency (RF) and microwave devices. Within the European Marie Curie project O-MOORE-NICE! (Operational Model Order Reduction for Nanoscale IC Electronics) we aim to investigate the feasibility of the surrogate modeling approach for entire RF circuit blocks. This paper presents an overview about the surrogate model type selection problem for low noise amplifier modeling. [ABSTRACT FROM AUTHOR]

Many complex, real world phenomena are difficult to study directly using controlled experiments. Instead, the use of computer simulations has become commonplace as a feasible alternative. However, due to the computational cost of these high fidelity simulations, the use of neural networks, kernel methods, and other surrogate modeling techniques have become indispensable. Surrogate models are compact and cheap to evaluate, and have proven very useful for tasks such as optimization, design space exploration, prototyping, and sensitivity analysis. Consequently, in many scientific fields there is great interest in techniques that facilitate the construction of such regression models, while minimizing the computational cost and maximizing model accuracy. This paper presents a fully automated machine learning toolkit for regression modeling and active learning to tackle these issues. A strong focus is placed on adaptivity, self-tuning and robustness in order to maximize efficiency and make the algorithms and tools easily accessible to other scientists in computational science and engineering. [ABSTRACT FROM AUTHOR]

The use of computer simulations has become a viable alternative for real-life controlled experiments. Due to the computational cost of these high fidelity simulations, surrogate models are often employed as an approximation for the original simulator. Because of their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, optimization, visualization and sensitivity analysis. Additionally, multiple surrogate models can be chained together to model large scale systems where the direct use of the original expensive simulators would be too cumbersome. Many surrogate model types, such as neural networks, support vector machines and rational models have been proposed, and many more techniques have been developed to minimize the number of expensive simulations required to train a sufficiently accurate surrogate model. In this chapter, we present a fully automated and integrated global surrogate modeling methodology for regression modeling and active learning that readily enables the adoption of advanced global surrogate modeling methods by application scientists. The work brings together insights from distributed systems, artificial intelligence, and modeling & simulation, and has applications in a very wide range of fields. The merits of this approach are illustrated with several examples, and several surrogate model types. [ABSTRACT FROM AUTHOR]

Because of its inter-organisational, collaborative use of computational resources, grid computing presents a severe interoperability challenge to grid application developers. Different middleware technologies need to be bridged in order to fully utilise the power the grid provides. This paper describes a bridge between two such middlewares: The H2O Metacomputing Framework and Jini technology. The paper details how H2O resources may be registered, discovered and used as JiniJini and Java are registered trademarks of Sun Microsystems. services. Both technologies are introduced, design decisions discussed and a fully functional implementation presented. [ABSTRACT FROM AUTHOR]