An adaptive artificial neural network-based generative design method for layout designs

Qian, Chao; Tan, Renkai; Ye, Wenjing

doi:10.1016/j.ijheatmasstransfer.2021.122313

Cited by 24 publications

(7 citation statements)

References 48 publications

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“…) (19) in which 𝑋 𝑐 and 𝑌 𝑐 represent the location of the center of the component over the substrate as shown in Fig. 1(b).…”

Section: Temperature Distribution In Multilayer Substratesmentioning

confidence: 99%

“…Another analysis reported in [8] handles squared components on top of square substrates connected to fin heat promoters. The extension of the methodology to compound neural networks is proposed in [19], but the extensive size of the datasets required for training affects the overall computational cost and time. To overcome this limit, the use of adaptive artificial neural networks is presented in [20,21]; nonetheless, when boundary conditions vary, the computational cost of this approach still looks high.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multivariable Optimization of Pyramidal Compound Substrates for Cooling of Power-Electronics in Modern Hybrid and Electric Propulsion Systems

Ghioldi

Hélie²,

Piscaglia³

2022

SSRN Journal

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“…) (19) in which 𝑋 𝑐 and 𝑌 𝑐 represent the location of the center of the component over the substrate as shown in Fig. 1(b).…”

Section: Temperature Distribution In Multilayer Substratesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariable Optimization of Pyramidal Compound Substrates for Cooling of Power-Electronics in Modern Hybrid and Electric Propulsion Systems

Ghioldi

Hélie²,

Piscaglia³

2022

SSRN Journal

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“…And this rationale enables the research of generative methods in the design community. Various research has been proposed to use the deep learning model for generative designs [26][27][28]. But it was not until recently that the generative models have been extended to unstructured data inputs i.e.…”

Section: Modeling For Design Generatormentioning

confidence: 99%

Graph Learning based Generative Design for Resilience of Interdependent Network Systems

Wu¹,

Wang²

2022

Preprint

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Interconnected complex systems usually undergo disruptions due to internal uncertainties and external negative impacts such as those caused by harsh operating environments or regional natural disaster events. To maintain the operation of interconnected network systems under both internal and external challenges, design for resilience research has been conducted from both enhancing the reliability of the system through better designs and improving the failure recovery capabilities. As for enhancing the designs, challenges have arisen for designing a robust system due to the increasing scale of modern systems and the complicated underlying physical constraints. To tackle these challenges and design a resilient system efficiently, this study presents a generative design method that utilizes graph learning algorithms. The generative design framework contains a performance estimator and a candidate design generator. The generator can intelligently mine good properties from existing systems and output new designs that meet predefined performance criteria. While the estimator can efficiently predict the performance of the generated design for a fast iterative learning process. Case studies results based on power systems from the IEEE dataset have illustrated the applicability of the proposed method for designing resilient interconnected systems.

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“…Sun J et al introduced a heat source layout reverse design method based on the deep learning show, attend, and read model [12]. Qian C et al presented a layout design method based on adaptive neural networks [13]. These methods have improved accuracy and assurance, but training costs continue to rise with increasing accuracy requirements.…”

Section: Introductionmentioning

confidence: 99%

Automatic Optimization System for Heat Source Layout of Multi-Chip Components Based on Multi-Software Integration

Huang,

Hu,

et al. 2024

Applied Sciences

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This paper presents an automatic optimization system for the heat source layout of multi-chip modules, referred to as IOS. The system aims to optimize the heat source layout of multi-chip modules through the integration of multiple software tools. The IOS utilizes an optimization algorithm, WINDOW DOS, and other methods to achieve this goal. Initially, two optimization strategies are introduced which are utilized in conjunction with the Non-dominated Sorting Genetic Algorithm-II(NSGA-II). Subsequently, a simplified thermal model of the multi-chip module is applied to the IOS. The automatic optimization module is then used to optimize the heat source layout. In the end, we simulated and analyzed the application results. The simulation analysis reveals that when the temperature extreme value index is optimized, the PCB board’s transient temperature range falls within [343.14 K, 344.09 K]. Similarly, when the temperature homogeneity index is optimized, the PCB board’s transient temperature range falls within [343.39 K, 344.21 K]. Comparing these results with the experience layout, the temperature difference is reduced by 76.36% and 79.60%, respectively. The optimization results demonstrate that the IOS has a significant impact, offering remarkable optimization effects, low cost, high efficiency, and a more uniform temperature distribution. These findings indicate that the IOS holds promising potential for applications and provides value in optimizing the heat source layout of multi-chip modules.

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An adaptive artificial neural network-based generative design method for layout designs

Cited by 24 publications

References 48 publications

Multivariable Optimization of Pyramidal Compound Substrates for Cooling of Power-Electronics in Modern Hybrid and Electric Propulsion Systems

Multivariable Optimization of Pyramidal Compound Substrates for Cooling of Power-Electronics in Modern Hybrid and Electric Propulsion Systems

Graph Learning based Generative Design for Resilience of Interdependent Network Systems

Automatic Optimization System for Heat Source Layout of Multi-Chip Components Based on Multi-Software Integration

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