Results 241 to 250 of about 133,335 (273)

Surrogate-Based Optimization of Biogeochemical Models

, 2010
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Data-Driven Optimization of Discontinuous and Continuous Fiber Composite Processes Using Machine Learning: A Review. [PDF]

Polymers (Basel)
Malashin I, Martysyuk D, Tynchenko V, Gantimurov A, Nelyub V, Borodulin A.   +5 more
europepmc   +1 more source

Machine learning based optimization of fly ash content for improving geopolymer concrete compressive strength. [PDF]

Sci Rep
Sichani MN, Dehkordi OM, Khorshidi M, Teimortashlu A, Nejatipour P.   +4 more
europepmc   +1 more source

Machine learning unlocks robust convergence for chemical process simulations

Jakobs D, Santos LF, Guillén-Gosálbez G.   +2 more
europepmc   +1 more source

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Surrogate based optimal waterflooding management

Journal of Petroleum Science and Engineering, 2013
Abstract In this work we solve the optimal waterflooding management problem using as design variables the rates allocated to each injector and producer well under different operational conditions. The duration of each control cycle may also be optimally controlled. The objective function is the net present value.
Bernardo Horowitz, Silvana Maria Bastos Afonso, Carlos Victor Paiva de Mendonça   +2 more
openaire   +1 more source

Surrogate‐based superstructure optimization framework

AIChE Journal, 2011
AbstractIn principle, optimization‐based “superstructure” methods for process synthesis can be more powerful than sequential‐conceptual methods as they account for all complex interactions between design decisions. However, these methods have not been widely adopted because they lead to mixed‐integer nonlinear programs that are hard to solve ...
Carlos A. Henao, Christos T. Maravelias
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Surrogate-Based Optimization

2014
In this chapter, the surrogate-based optimization (SBO) paradigm is formulated. We discuss SBO on a generic level, including the optimization flow, fundamental properties of the SBO process, and typical ways of constructing the surrogate. We emphasize a distinction between function approximation and physics-based surrogates as well as discuss the ...
Slawomir Koziel, Stanislav Ogurtsov
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Handling Constraints in Surrogate-Based Optimization

53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR>20th AIAA/ASME/AHS Adaptive Structures Conference<BR>14th AIAA, 2012
Surrogate (meta or response surface) models are frequently used to emulate expensive computer simulations. In global optimization, surrogate-based approaches accelerate the optimization process that would otherwise suffer from intractable run times. In many cases, design constraints are also expensive to evaluate and replaced with surrogates.
James Parr, Alexander Forrester, Andy Keane, Carren Holden   +3 more
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Setting targets for surrogate-based optimization

Journal of Global Optimization, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Queipo, Nestor V., Pintos, Salvador, Nava, Efrain   +2 more
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Surrogate-based Multi-Objective Particle Swarm Optimization

2008 IEEE Swarm Intelligence Symposium, 2008
This paper presents a new algorithm that approximates real function evaluations using supervised learning with a surrogate method called support vector machine (SVM). We perform a comparative study among different leader selection schemes in a multi-objective particle swarm optimizer (MOPSO), in order to determine the most appropriate approach to be ...
Luis V. Santana-Quintero, Carlos A. Coello Coello, Alfredo G. Hernandez-Diaz, Jesus Moises Osorio Velazquez   +3 more
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