Inferring the properties of black holes and neutron stars is a key science goal gravitational-wave (GW) astronomy. To extract as much information possible from GW observations, we must develop methods to reduce cost Bayesian inference. In this paper, use artificial neural networks (ANNs) parallelization power graphics processing units (GPUs) improve surrogate modeling method, which can produce ...

Stochastic, iterative search methods such as Evolutionary Algorithms (EAs) require evaluation of the candidates which may be prohibitively expensive in many real world optimization problems. Use of meta-models or surrogates is being experimented to reduce the number of such evaluations. In this paper we investigated two such methods. The first method (DAFHEA) partially replaces expensive functi...

Agent-based models (ABMs) have become an increasingly important mode of inquiry for the life sciences. They are particularly valuable for systems that are not understood well enough to build an equation-based model. These advantages, however, are counterbalanced by the difficulty of analyzing and using ABMs, due to the lack of the type of mathematical tools available for more traditional models...

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation integrated in multi-level a and energy system superstructure with surrogate models, applying active le...

The optimal design methodologies in aeronautics are known to be constrained by the computational burden required direct simulations. Due this reason, development of efficient metamodelling techniques represents nowadays an imperative need for designers. In fact, surrogate models has been demonstrated significantly reduce number high-fidelity evaluations, thus alleviating computing effort. Over ...

Designing optimal experiments minimizes the uncertainty of results and maximizes efficient use resources. Herein, machine learning surrogate models approximate coordinate exchange (ACE) algorithm are used to determine experimental designs (OEDs) over large or arbitrarily restrictive design spaces. OED is particularly salient in materials science, where expensive material properties must often b...

Surrogate-based-optimization methods provide a means to minimize expensive highfidelity models at reduced computational cost. The methods are useful in problems for which two models of the same physical system exist: a high-fidelity model which is accurate and expensive, and a low-fidelity model which is less costly but less accurate. A number of model management techniques have been developed ...