Optimizing the magnetic circuit of an actuator

Popescu, Lizeta; Diodiu, Lucian

doi:10.1051/matecconf/201929001013

Cited by 1 publication

(1 citation statement)

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“…A magnetic circuit is a path in which a magnetic field can be enclosed, as shown in Figure 6 a. These circuits can be modeled by computer simulation and have been used to optimize the circuit’s performance [ 60 ]. The magnetic circuit is described by a CAE simulation in COMSOL Multiphysics ® , shown in Figure 6 b.…”

Section: Case Studiesmentioning

confidence: 99%

ASAMS: An Adaptive Sequential Sampling and Automatic Model Selection for Artificial Intelligence Surrogate Modeling

Duchanoy

Calvo

Moreno-Armendáriz

2020

Sensors

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Surrogate Modeling (SM) is often used to reduce the computational burden of time-consuming system simulations. However, continuous advances in Artificial Intelligence (AI) and the spread of embedded sensors have led to the creation of Digital Twins (DT), Design Mining (DM), and Soft Sensors (SS). These methodologies represent a new challenge for the generation of surrogate models since they require the implementation of elaborated artificial intelligence algorithms and minimize the number of physical experiments measured. To reduce the assessment of a physical system, several existing adaptive sequential sampling methodologies have been developed; however, they are limited in most part to the Kriging models and Kriging-model-based Monte Carlo Simulation. In this paper, we integrate a distinct adaptive sampling methodology to an automated machine learning methodology (AutoML) to help in the process of model selection while minimizing the system evaluation and maximizing the system performance for surrogate models based on artificial intelligence algorithms. In each iteration, this framework uses a grid search algorithm to determine the best candidate models and perform a leave-one-out cross-validation to calculate the performance of each sampled point. A Voronoi diagram is applied to partition the sampling region into some local cells, and the Voronoi vertexes are considered as new candidate points. The performance of the sample points is used to estimate the accuracy of the model for a set of candidate points to select those that will improve more the model’s accuracy. Then, the number of candidate models is reduced. Finally, the performance of the framework is tested using two examples to demonstrate the applicability of the proposed method.

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Section: Case Studiesmentioning

confidence: 99%