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Found 19 papers, 6 papers with code
Multi-scale Topology Optimization using Neural Networks
The neural network takes as input the local coordinates within a cell to represent the density distribution within a cell, as well as the global coordinates of each cell to design spatially varying microstructure cells.
Curve-based Neural Style Transfer
This research presents a new parametric style transfer framework specifically designed for curve-based design sketches.
Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition
In the first case study, BIGNet not only classifies phone brands but also captures brand-related features across multiple scales, such as the location of the lens, the height-width ratio, and the screen-frame gap, as confirmed by AI evaluation.
Topology Optimization using Neural Networks with Conditioning Field Initialization for Improved Efficiency
We employ the strain energy field calculated on the initial design domain as an additional conditioning field input to the neural network throughout the optimization.
DMF-TONN: Direct Mesh-free Topology Optimization using Neural Networks
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.
Target specific peptide design using latent space approximate trajectory collector
Despite the prevalence and many successes of deep learning applications in de novo molecular design, the problem of peptide generation targeting specific proteins remains unsolved.
Component Segmentation of Engineering Drawings Using Graph Convolutional Networks
We test our framework in the context of semantic segmentation of text, dimension and, contour components in engineering drawings.
Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron
Our automatic power plane generation approach is based on genetic optimization combined with a multilayer perceptron and is able to automatically generate power planes across a diverse set of problems with varying levels of difficulty.
Concurrent build direction, part segmentation, and topology optimization for additive manufacturing using neural networks
Through training, the network learns a material density and segment classification in the continuous 3D space.
Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing
Physical design and production of Integrated Circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing.
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision
The key advantage of this approach is that the router can learn a policy in an offline setting with supervision, while improving the run-time performance nearly 100x over the genetic solver.
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning
We also discover a similarity between the attention router and the baseline genetic router in terms of positive correlations in cost and routing patterns, which demonstrate the attention router's ability to be utilized not only as a detailed router but also as a predictor for routability and congestion.
TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain
In topology optimization using deep learning, load and boundary conditions represented as vectors or sparse matrices often miss the opportunity to encode a rich view of the design problem, leading to less than ideal generalization results.
Structural Design Using Laplacian Shells
We introduce a method to design lightweight shell objects that are structurally robust under the external forces they may experience during use.
A Deep Reinforcement Learning Approach for Global Routing
At the heart of the proposed method is deep reinforcement learning that enables an agent to produce an optimal policy for routing based on the variety of problems it is presented with leveraging the conjoint optimization mechanism of deep reinforcement learning.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs.
Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty
Our approach uses a computationally tractable experimental design method to select number of sample force locations based on geometry only, without inspecting the stress response that requires computationally expensive finite-element analysis.
Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
The demand for fast and accurate structural analysis is becoming increasingly more prevalent with the advance of generative design and topology optimization technologies.
Data-driven Upsampling of Point Clouds
High quality upsampling of sparse 3D point clouds is critically useful for a wide range of geometric operations such as reconstruction, rendering, meshing, and analysis.
