Sequential optimization of strip bending process using multiquadric radial basis function surrogate models [PDF]
, 2013 Surrogate models are used within the sequential optimization strategy for forming processes. A sequential improvement (SI) scheme is used to refine the surrogate model in the optimal region. One of the popular surrogate modeling methods for SI is Kriging.
Boogaard, A.H. van den +2 more
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Grid Enabled Surrogate Modeling [PDF]
, 2009 The simulation and optimization of complex systems is a very time consuming and computationally intensive task. Therefore, global surrogate modeling methods are often used for the efficient exploration of the design space, as they reduce the number of simulations needed.
Gorissen, Dirk +3 more
openaire +3 more sources
Classical Surrogates for Quantum Learning Models
Physical Review Letters, 2023 The advent of noisy intermediate-scale quantum computers has put the search for possible applications to the forefront of quantum information science. One area where hopes for an advantage through near-term quantum computers are high is quantum machine learning, where variational quantum learning models based on parametrized quantum circuits are ...
Franz J. Schreiber +2 more
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Surrogate modeling a computational fluid dynamics-based wind turbine wake simulation using machine learning [PDF]
, 2017 The Wind Farm Layout Optimization problem involves finding the optimal positions for wind turbines on a wind farm site. Current Metahueristic based methods make use of a combination of turbine specifications and parameters, mathematical models and ...
Mayo, Michael +2 more
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Surrogate modeling of RF circuit blocks [PDF]
, 2010 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 ...
Croon, Jeroen A +3 more
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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
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Dynamical model of surrogate reactions [PDF]
Physical Review C, 2011 17 pages, 5 ...
Y. Aritomo, S. Chiba, K. Nishio
openaire +2 more sources
A Surrogate Model of Gravitational Waveforms from Numerical Relativity Simulations of Precessing Binary Black Hole Mergers [PDF]
, 2017 We present the first surrogate model for gravitational waveforms from the coalescence of precessing binary black holes. We call this surrogate model NRSur4d2s. Our methodology significantly extends recently introduced reduced-order and surrogate modeling
Blackman, Jonathan +6 more
core +3 more sources
, 2020 Bayesian inverse modeling is important for a better understanding of hydrological processes. However, this approach can be computationally demanding, as it usually requires a large number of model evaluations.
Chen, Dingjiang +4 more
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Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models [PDF]
, 2015 Simulating a binary black hole (BBH) coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time.
Blackman, Jonathan +6 more
core +2 more sources