Several applications such as nuclear forensics, fuel cycle simulations and sensitivity analysis require methods to quickly compute spent nuclide compositions for various irradiation histories. Traditionally, this has been done by interpolating between one-group cross-sections that have pre-computed from reactor a grid of input parameters, using fits Cubic Spline. We propose the use Gaussian Pro...

We review the state of the art in space mapping (SM) and the SM-based surrogate (modeling) concepts. We describe the input, implicit and output SM techniques. We present an SM framework and its applications in engineering modeling and design optimization. Significant examples of recent implementations of SM are reviewed.

Numerical relativity (NR) simulations of binary black hole (BBH) systems provide the most accurate gravitational wave predictions, but at a high computational cost -- especially when holes have nearly extremal spins (i.e. near theoretical upper limit) or very unequal masses. Recently, technique Reduced Order Modeling (ROM) has enabled construction surrogate models trained on an existing set NR ...

Integrated management of water reuse technologies and coordinated operations with other system components is fundamental to fully exploiting potential. Yet, these are primarily designed considering their individual efficiency more than possible synergies traditional practices. In this paper, we introduce a general-purpose framework that couples physical surrogate modelling optimal control metho...

When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face trade-off between accuracy and efficiency. Especially for parametrized, time dependent problems in engineering computations, it is often case acceptable computational budgets limit availability high-fidelity, accurate simulation data. Multi-fidelity surrogate modeling has emerged as...

The pipeline optimization problem in machine learning requires simultaneous of structures and parameter adaptation their elements. Having an elegant way to express these can help lessen the complexity management analysis performances together with different choices strategies. With issues mind, we created AutoMLPipeline (AMLP) toolkit which facilitates creation evaluation complex using simple e...

When dealing with computationally expensive simulation codes or process measurement data, global surrogate modeling methods are firmly established as facilitators for design space exploration, sensitivity analysis, visualization and optimization. Popular surrogate model types include neural networks, support vector machines, and splines. In addition, the cost of each simulation mandates the use...

Most publications on surrogate models have focused either on the prediction quality or on the optimization performance. It is still unclear whether the prediction quality is indeed related to the suitability for optimization. Moreover, most of these studies only employ lowdimensional test cases. There are no results for popular surrogate models, such as kriging, for high-dimensional (n > 10) no...

This short chapter introduces the SURROGATE data type, which is useful when modeling time-varying objects. Surrogates are unique identifiers that can be compared for equality, but the values of which cannot be seen by the users. In this sense, a surrogate is “pure” identity and does not describe a property (i.e., it has no observable value). For this reason, a SURROGATE data type cannot be trea...