145 citations · 339 across the 28 of their papers we have counts for
11 papers · 1 filter
MDDM: A Molecular Dynamics Diffusion Model to Predict Particle Self-Assembly
Kevin Ferguson, Yu-hsuan Chen, Levent Burak Kara
The discovery and study of new material systems rely on molecular simulations that often come with significant computational expense. We propose MDDM, a Molecular Dynamics Diffusio…
Topology-Agnostic Graph U-Nets for Scalar Field Prediction on Unstructured Meshes
Kevin Ferguson, Yu-hsuan Chen, Yiming Chen +3
Machine-learned surrogate models to accelerate lengthy computer simulations are becoming increasingly important as engineers look to streamline the product design cycle. In many ca…
Scalar Field Prediction on Meshes Using Interpolated Multi-Resolution Convolutional Neural Networks
Kevin Ferguson, Andrew Gillman, James Hardin +1
Scalar fields, such as stress or temperature fields, are often calculated in shape optimization and design problems in engineering. For complex problems where shapes have varying t…
VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimation
Yu-hsuan Chen, Jonathan Cagan, Levent Burak kara
Designing for manufacturing poses significant challenges in part due to the computation bottleneck of Computer-Aided Manufacturing (CAM) simulations. Although deep learning as an a…
Topology Optimization using Neural Networks with Conditioning Field Initialization for Improved Efficiency
Hongrui Chen, Aditya Joglekar, Levent Burak Kara
We propose conditioning field initialization for neural network based topology optimization. In this work, we focus on (1) improving upon existing neural network based topology opt…
Concurrent build direction, part segmentation, and topology optimization for additive manufacturing using neural networks
Hongrui Chen, Aditya Joglekar, Kate S. Whitefoot +1
We propose a neural network-based approach to topology optimization that aims to reduce the use of support structures in additive manufacturing. Our approach uses a network archite…
Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision
Haiguang Liao, Qingyi Dong, Weiyi Qi +2
Detailed routing is one of the most critical steps in analog circuit design. Complete routing has become increasingly more challenging in advanced node analog circuits, making adva…
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning
Haiguang Liao, Qingyi Dong, Xuliang Dong +5
In the physical design of integrated circuits, global and detailed routing are critical stages involving the determination of the interconnected paths of each net on a circuit whil…
A Deep Reinforcement Learning Approach for Global Routing
Haiguang Liao, Wentai Zhang, Xuliang Dong +3
Global routing has been a historically challenging problem in electronic circuit design, where the challenge is to connect a large and arbitrary number of circuit components with w…
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
Wentai Zhang, Zhangsihao Yang, Haoliang Jiang +5
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. The…
3D Topology Optimization using Convolutional Neural Networks
Saurabh Banga, Harsh Gehani, Sanket Bhilare +2
Topology optimization is computationally demanding that requires the assembly and solution to a finite element problem for each material distribution hypothesis. As a complementary…