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Found 13 papers, 2 papers with code
Deep Generative Design for Mass Production
Generative Design (GD) has evolved as a transformative design approach, employing advanced algorithms and AI to create diverse and innovative solutions beyond traditional constraints.
Deep Generative Model-based Synthesis of Four-bar Linkage Mechanisms with Target Conditions
The proposed model is based on a conditional generative adversarial network (cGAN) with modifications for mechanism synthesis, which is trained to learn the relationship between the requirements of a mechanism with respect to linkage lengths.
Data-Driven Physics-Informed Neural Networks: A Digital Twin Perspective
This study explores the potential of physics-informed neural networks (PINNs) for the realization of digital twins (DT) from various perspectives.
Weighted Unsupervised Domain Adaptation Considering Geometry Features and Engineering Performance of 3D Design Data
The developed bi-weighting strategy based on the geometry features and engineering performance of engineering structures is incorporated into the training process.
Performance Comparison of Design Optimization and Deep Learning-based Inverse Design
Surrogate model-based optimization has been increasingly used in the field of engineering design.
Topology Optimization via Machine Learning and Deep Learning: A Review
Topology optimization (TO) is a method of deriving an optimal design that satisfies a given load and boundary conditions within a design domain.
Adaptive Neural Network Ensemble Using Frequency Distribution
Neural network (NN) ensembles can reduce large prediction variance of NN and improve prediction accuracy.
Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress
The proposed model can replace the impact test in the early wheel-development stage by predicting the impact performance in real-time and can be used without domain knowledge.
Deep Learning-Based Inverse Design for Engineering Systems: Multidisciplinary Design Optimization of Automotive Brakes
The braking performance of the brake system is a target performance that must be considered for vehicle development.
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature Visualization
Using the proposed process, we can provide a design guidance to engineering designers in reducing manufacturing costs during the conceptual design phase.
Idle Vehicle Relocation Strategy through Deep Learning for Shared Autonomous Electric Vehicle System Optimization
Finally, a deep learning model using the optimal solution data is built to estimate the optimal strategy without solving relocation.
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs
To reduce the heavy computational burden of the wheel topology optimization process required by our RL formulation, we approximate the optimization process with neural networks.
Deep Generative Design: Integration of Topology Optimization and Generative Models
Deep learning has recently been applied to various research areas of design optimization.
