Search Results for author: Levent Burak Kara

Found 19 papers, 6 papers with code

Multi-scale Topology Optimization using Neural Networks

no code implementations11 Apr 2024 Hongrui Chen, Xingchen Liu, Levent Burak Kara

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

no code implementations3 Oct 2023 Yu-Hsuan Chen, Levent Burak Kara, Jonathan Cagan

This research presents a new parametric style transfer framework specifically designed for curve-based design sketches.

Style Transfer

Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition

2 code implementations5 Jun 2023 Yu-Hsuan Chen, Levent Burak Kara, Jonathan Cagan

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.

Graph Neural Network Vector Graphics

Topology Optimization using Neural Networks with Conditioning Field Initialization for Improved Efficiency

1 code implementation17 May 2023 Hongrui Chen, Aditya Joglekar, Levent Burak Kara

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

1 code implementation6 May 2023 Aditya Joglekar, Hongrui Chen, Levent Burak Kara

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

no code implementations2 Feb 2023 Tong Lin, Sijie Chen, Ruchira Basu, Dehu Pei, Xiaolin Cheng, Levent Burak Kara

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.

Hierarchical Automatic Power Plane Generation with Genetic Optimization and Multilayer Perceptron

no code implementations28 Oct 2022 Haiguang Liao, Vinay Patil, Xuliang Dong, Devika Shanbhag, Elias Fallon, Taylor Hogan, Mirko Spasojevic, Levent Burak Kara

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.

Contour Detection

Placement in Integrated Circuits using Cyclic Reinforcement Learning and Simulated Annealing

no code implementations15 Nov 2020 Dhruv Vashisht, Harshit Rampal, Haiguang Liao, Yang Lu, Devika Shanbhag, Elias Fallon, Levent Burak Kara

Physical design and production of Integrated Circuits (IC) is becoming increasingly more challenging as the sophistication in IC technology is steadily increasing.

reinforcement-learning Reinforcement Learning (RL)

Track-Assignment Detailed Routing Using Attention-based Policy Model With Supervision

no code implementations26 Oct 2020 Haiguang Liao, Qingyi Dong, Weiyi Qi, Elias Fallon, Levent Burak Kara

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.

Reinforcement Learning (RL)

Attention Routing: track-assignment detailed routing using attention-based reinforcement learning

no code implementations20 Apr 2020 Haiguang Liao, Qingyi Dong, Xuliang Dong, Wentai Zhang, Wangyang Zhang, Weiyi Qi, Elias Fallon, Levent Burak Kara

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.

reinforcement-learning Reinforcement Learning (RL)

TopologyGAN: Topology Optimization Using Generative Adversarial Networks Based on Physical Fields Over the Initial Domain

1 code implementation5 Mar 2020 Zhenguo Nie, Tong Lin, Haoliang Jiang, Levent Burak Kara

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.

Generative Adversarial Network

Structural Design Using Laplacian Shells

no code implementations25 Jun 2019 Erva Ulu, James McCann, Levent Burak Kara

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

1 code implementation20 Jun 2019 Haiguang Liao, Wentai Zhang, Xuliang Dong, Barnabas Poczos, Kenji Shimada, Levent Burak Kara

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.

reinforcement-learning Reinforcement Learning (RL)

3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

no code implementations16 Apr 2019 Wentai Zhang, Zhangsihao Yang, Haoliang Jiang, Suyash Nigam, Soji Yamakawa, Tomotake Furuhata, Kenji Shimada, Levent Burak Kara

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.

3D Shape Representation Decoder

Efficient Load Sampling for Worst-Case Structural Analysis Under Force Location Uncertainty

no code implementations25 Oct 2018 Yining Wang, Erva Ulu, Aarti Singh, Levent Burak Kara

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.

Experimental Design

Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks

1 code implementation27 Aug 2018 Zhenguo Nie, Haoliang Jiang, Levent Burak Kara

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

no code implementations8 Jul 2018 Wentai Zhang, Haoliang Jiang, Zhangsihao Yang, Soji Yamakawa, Kenji Shimada, Levent Burak Kara

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.

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