58,212 research outputs found
Data-driven Topology Optimization of Channel Flow Problems
Typical topology optimization methods require complex iterative calculations,
which cannot be realized in meeting the requirements of fast computing
applications. The neural network is studied to reduce the time of computing the
optimization result, however, the data-driven method for fluid topology
optimization is less of discussion. This paper intends to introduce a neural
network architecture that avoids time-consuming iterative processes and has a
strong generalization ability for topology optimization for Stokes flow.
Different neural network methods that have been already successfully used in
solid structure optimization problems are mutated and examined for fluid
topology optimization cases, including Convolution Neural Networks (CNN),
conditional Generative Adversarial Networks (cGAN), and Denoising Diffusion
Implicit Models (DDIM). The presented neural network method is tested on the
channel flow topology optimization problems for Stokes flow. The results have
shown that our presented method has high pixel accuracy, and we gain a 663
times decrease in execution time compared with the conventional method on
average
Concurrent build direction, part segmentation, and topology optimization for additive manufacturing using neural networks
We propose a neural network-based approach to topology optimization that aims
to reduce the use of support structures in additive manufacturing. Our approach
uses a network architecture that allows the simultaneous determination of an
optimized: (1) part segmentation, (2) the topology of each part, and (3) the
build direction of each part that collectively minimize the amount of support
structure. Through training, the network learns a material density and segment
classification in the continuous 3D space. Given a problem domain with
prescribed load and displacement boundary conditions, the neural network takes
as input 3D coordinates of the voxelized domain as training samples and outputs
a continuous density field. Since the neural network for topology optimization
learns the density distribution field, analytical solutions to the density
gradient can be obtained from the input-output relationship of the neural
network. We demonstrate our approach on several compliance minimization
problems with volume fraction constraints, where support volume minimization is
added as an additional criterion to the objective function. We show that
simultaneous optimization of part segmentation along with the topology and
print angle optimization further reduces the support structure, compared to a
combined print angle and topology optimization without segmentation
DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks
We propose a direct mesh-free method for performing topology optimization by
integrating a density field approximation neural network with a displacement
field approximation neural network. We show that this direct integration
approach can give comparable results to conventional topology optimization
techniques, with an added advantage of enabling seamless integration with
post-processing software, and a potential of topology optimization with
objectives where meshing and Finite Element Analysis (FEA) may be expensive or
not suitable. Our approach (DMF-TONN) takes in as inputs the boundary
conditions and domain coordinates and finds the optimum density field for
minimizing the loss function of compliance and volume fraction constraint
violation. The mesh-free nature is enabled by a physics-informed displacement
field approximation neural network to solve the linear elasticity partial
differential equation and replace the FEA conventionally used for calculating
the compliance. We show that using a suitable Fourier Features neural network
architecture and hyperparameters, the density field approximation neural
network can learn the weights to represent the optimal density field for the
given domain and boundary conditions, by directly backpropagating the loss
gradient through the displacement field approximation neural network, and
unlike prior work there is no requirement of a sensitivity filter, optimality
criterion method, or a separate training of density network in each topology
optimization iteration
Multi-population genetic algorithms with immigrants scheme for dynamic shortest path routing problems in mobile ad hoc networks
Copyright @ Springer-Verlag Berlin Heidelberg 2010.The static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless mesh network, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem in mobile wireless networks. In this paper, we propose to use multi-population GAs with immigrants scheme to solve the dynamic SP problem in MANETs which is the representative of new generation wireless networks. The experimental results show that the proposed GAs can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council(EPSRC) of UK under Grant EP/E060722/1
Genetic algorithms with elitism-based immigrants for dynamic shortest path problem in mobile ad hoc networks
This article is posted here with permission from the IEEE - Copyright @ 2009 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks (ANNs), genetic algorithms (GAs), particle swarm optimization (PSO), etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile ad hoc network (MANET), wireless sensor network (WSN), etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, that is, the network topology changes over time due to energy conservation or node mobility. Therefore, the SP problem turns out to be a dynamic optimization problem (DOP) in MANETs. In this paper, we propose to use elitism-based immigrants GA (EIGA) to solve the dynamic SP problem in MANETs. We consider MANETs as target systems because they represent new generation wireless networks. The experimental results show that the EIGA can quickly adapt to the environmental changes (i.e., the network topology change) and produce good solutions after each change.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1
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