Results 21 to 30 of about 461,664 (195)
Data-efficient Neuroevolution with Kernel-Based Surrogate Models [PDF]
, 2018 Surrogate-assistance approaches have long been used in computationally expensive domains to improve the data-efficiency of optimization algorithms. Neuroevolution, however, has so far resisted the application of these techniques because it requires the ...
Lehman J +4 more
core +3 more sources
The SUMO toolbox: a tool for automatic regression modeling and active learning [PDF]
, 2013 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.
Couckuyt, Ivo +4 more
core +2 more sources
Surrogate Models for Efficient Multi-Objective Optimization of Building Performance
Energies, 2023 Nowadays, the large set of available simulation tools brings numerous benefits to urban and architectural practices. However, simulations often take a considerable amount of time to yield significant results, particularly when performing many simulations
Gonçalo Roque Araújo +4 more
doaj +1 more source
Spatio-Temporal Gradient Enhanced Surrogate Modeling Strategies
Mathematical and Computational Applications, 2023 This research compares the performance of space-time surrogate models (STSMs) and network surrogate models (NSMs). Specifically, when the system response varies over time (or pseudo-time), the surrogates must predict the system response.
Johann M. Bouwer +2 more
doaj +1 more source
IEEE Access, 2022 The current design process of mooring systems for Floating Production, Storage, and Offloading units (FPSOs) depends on the availability of the platform’s mathematical model and the accuracy of dynamic simulations.
Lucas P. Cotrim +5 more
doaj +1 more source
On the use of gradients in Kriging surrogate models [PDF]
Proceedings of the Winter Simulation Conference 2014, 2014 The use of Kriging surrogate models has become popular in approximating computation-intensive deterministic computer models. In this work, the effect of enhancing Kriging surrogate models with a (partial) set of gradients is investigated. While, intuitively, gradient information is useful to enhance prediction accuracy, another motivation behind this ...
Selvakumar Ulaganathan +4 more
openaire +2 more sources
Universal Prediction Distribution for Surrogate Models [PDF]
SIAM/ASA Journal on Uncertainty Quantification, 2017 The use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions.
Ben Salem, Malek +3 more
openaire +5 more sources
Particle filter-based Gaussian process optimisation for parameter inference [PDF]
, 2014 We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps.
Dahlin, Johan, Lindsten, Fredrik
core +2 more sources
Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments
Mathematics, 2022 For computationally intensive problems, data-driven evolutionary algorithms (DDEAs) are advantageous for low computational budgets because they build surrogate models based on historical data to approximate the expensive evaluation.
Dalue Lin +3 more
doaj +1 more source
A surrogate FRAX model for Nepal [PDF]
Archives of OsteoporosisAbstract Summary A surrogate FRAX® model for Nepal has been constructed using age- and sex-specific hip fracture rates for Indians living in Singapore and age- and sex-specific mortality rates from Nepal. Introduction FRAX
Johansson, H. +5 more
openaire +5 more sources