Deep Generative Design: Integration of Topology Optimization and Generative Models

Abstract

Deep learning has recently been applied to various research areas of design optimization. This study presents the need and effectiveness of adopting deep learning for generative design (or design exploration) research area. This work proposes an artificial intelligent (AI)-based design automation framework that is capable of generating numerous design options which are not only aesthetic but also optimized for engineering performance. The proposed framework integrates topology optimization and deep generative models (e.g., generative adversarial networks (GANs)) in an iterative manner to explore new design options, thus generating a large number of designs starting from limited previous design data. In addition, anomaly detection can evaluate the novelty of generated designs, thus helping designers choose among design options. The 2D wheel design problem is applied as a case study for validation of the proposed framework. The framework manifests better aesthetics, diversity, and robustness of generated designs than previous generative design methods.

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• Deep Learning in Deterministic Computational Mechanics
• Self-Directed Online Machine Learning for Topology Optimization
• Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs
• 2022 Review of Data-Driven Plasma Science
• Data-Driven Design-by-Analogy: State of the Art and Future Directions
• ShipHullGAN: A generic parametric modeller for ship hull design using deep convolutional generative model
• Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
• Data-driven topology design using a deep generative model
• MO-PaDGAN: Reparameterizing Engineering Designs for Augmented Multi-objective Optimization
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges
• De-homogenization using Convolutional Neural Networks
• SimJEB: Simulated Jet Engine Bracket Dataset
• Goal-Directed Design Agents: Integrating Visual Imitation with One-Step Lookahead Optimization for Generative Design
• ChatGPT as an inventor: Eliciting the strengths and weaknesses of current large language models against humans in engineering design
• Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions
• Generative Design of Physical Objects using Modular Framework
• Generative Design for Performance Enhancement, Weight Reduction, and its Industrial Implications
• Data-driven multifidelity topology design with multi-channel variational auto-encoder for concurrent optimization of multiple design variable fields
• Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials
• Gradient-free neural topology optimization: Towards effective fracture-resistant designs
• Computational Rational Engineering and Development: Synergies and Opportunities
• A Parametric Level Set Method for Topology Optimization based on Deep Neural Network (DNN)
• Deep Concept Identification for Generative Design
• TripOptimizer: Generative 3D Shape Optimization and Drag Prediction using Triplane VAE Networks
• Enhanced Data-driven Topology Design Methodology with Multi-level Mesh and Correlation-based Mutation for Stress-related Multi-objective Optimization
• A study on using image based machine learning methods to develop the surrogate models of stamp forming simulations
• I-nteract 2.0: A Cyber-Physical System to Design 3D Models using Mixed Reality Technologies and Deep Learning for Additive Manufacturing