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Real-Time Generative Design of Diverse, "Truly" Optimized Structures With Controllable Structural Complexities

https://doi.org/10.2139/ssrn.4683967
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Topology Optimization: Theory, Methods, and Applications
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Balancing diversity and performance in global optimization
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Diverse competitive design for topology optimization
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Deep learning for determining a near-optimal topological design without any iteration
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A diversity metric based on Gaussian process model for diverse and competitive design
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An efficient and easy-to-extend Matlab code of the Moving Morphable Component (MMC) method for three-dimensional topology optimization
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Improving the diversity of topology-optimized designs by swarm intelligence
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On the use of artificial neural networks in topology optimisation
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Explicit 2D topological control using SIMP and MMA in structural topology optimization
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Topology Optimization in Aircraft and Aerospace Structures Design
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One-shot generation of near-optimal topology through theory-driven machine learning
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Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs
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Explicit structural topology optimization based on moving morphable components (MMC) with curved skeletons
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Parameterized level-set based topology optimization method considering symmetry and pattern repetition constraints
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Explicit control of 2D and 3D structural complexity by discrete variable topology optimization method
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A unified framework for explicit layout/topology optimization of thin-walled structures based on Moving Morphable Components (MMC) method and adaptive ground structure approach
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Simple and effective strategies for achieving diverse and competitive structural designs
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Problem-independent machine learning (PIML)-based topology optimization—A universal approach
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A Problem-Independent Machine Learning (PIML) enhanced substructure-based approach for large-scale structural analysis and topology optimization of linear elastic structures
inferredcites →https://doi.org/10.1016/j.engstruct.2023.116376openalex-extracted
Real-Time structure topology optimization using CNN driven Moving Morphable component method
inferredcites →https://doi.org/10.1016/j.finel.2022.103779openalex-extracted
A thinning algorithm based approach to controlling structural complexity in topology optimization
inferredcites →https://doi.org/10.1016/j.promfg.2020.02.251openalex-extracted
DzAIℕ: Deep learning based generative design
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A deep convolutional neural network for topology optimization with perceptible generalization ability
inferredcites →https://doi.org/10.1115/1.4027609openalex-extracted
Doing Topology Optimization Explicitly and Geometrically—A New Moving Morphable Components Based Framework
inferredcites →https://doi.org/10.1115/1.4041319openalex-extracted
Machine Learning-Driven Real-Time Topology Optimization Under Moving Morphable Component-Based Framework
inferredcites →https://doi.org/10.1115/1.4044229openalex-extracted
Deep Generative Design: Integration of Topology Optimization and Generative Models
inferredcites →https://doi.org/10.1115/1.4049533openalex-extracted
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
inferredcites →https://doi.org/10.1145/3422622openalex-extracted
Generative adversarial networks
inferredcites →https://doi.org/10.18502/keg.v2i2.612openalex-extracted
Generative Design: What it is? How is it being used? Why it’s a game changer
inferredcites →https://doi.org/10.2139/ssrn.4194695openalex-extracted
Explicit 3d Topological Control Using Simp and Mma in Structural Topology Optimization
inferredcites →https://doi.org/10.48550/arxiv.1704.00028openalex-extracted
Improved Training of Wasserstein GANs
inferredcites →https://doi.org/10.48550/arxiv.2003.04685openalex-extracted
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
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