Zobrazeno 1 - 10
of 342
pro vyhledávání: '"Kara Levent"'
Autor:
Ferguson, Kevin, Chen, Yu-hsuan, Chen, Yiming, Gillman, Andrew, Hardin, James, Kara, Levent Burak
Machine-learned surrogate models to accelerate lengthy computer simulations are becoming increasingly important as engineers look to streamline the product design cycle. In many cases, these approaches offer the ability to predict relevant quantities
Externí odkaz:
http://arxiv.org/abs/2410.06406
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 topology and cannot be parametrized, data-driven scalar field predictio
Externí odkaz:
http://arxiv.org/abs/2410.05522
Autor:
Chen, Hongrui, Joglekar, Aditya, Rubinstein, Zack, Schmerl, Bradley, Fedder, Gary, de Nijs, Jan, Garlan, David, Smith, Stephen, Kara, Levent Burak
Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for ma
Externí odkaz:
http://arxiv.org/abs/2409.03089
Designing for manufacturing poses significant challenges in part due to the computation bottleneck of Computer-Aided Manufacturing (CAM) simulations. Although deep learning as an alternative offers fast inference, its performance is dependently bound
Externí odkaz:
http://arxiv.org/abs/2406.12286
Reduced order modeling lowers the computational cost of solving PDEs by learning a low-order spatial representation from data and dynamically evolving these representations using manifold projections of the governing equations. While commonly used, l
Externí odkaz:
http://arxiv.org/abs/2405.14890
A long-standing challenge is designing multi-scale structures with good connectivity between cells while optimizing each cell to reach close to the theoretical performance limit. We propose a new method for direct multi-scale topology optimization us
Externí odkaz:
http://arxiv.org/abs/2404.08708
Autor:
Liu, Haolin, Gobert, Christian, Ferguson, Kevin, Abranovic, Brandon, Chen, Hongrui, Beuth, Jack L., Rollett, Anthony D., Kara, Levent Burak
With a growing demand for high-quality fabrication, the interest in real-time process and defect monitoring of laser powder bed fusion (LPBF) has increased, leading manufacturers to incorporate a variety of online sensing methods including acoustic s
Externí odkaz:
http://arxiv.org/abs/2310.05289
Automating Style Analysis and Visualization With Explainable AI -- Case Studies on Brand Recognition
Incorporating style-related objectives into shape design has been centrally important to maximize product appeal. However, stylistic features such as aesthetics and semantic attributes are hard to codify even for experts. As such, algorithmic style c
Externí odkaz:
http://arxiv.org/abs/2306.03021
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 optimization, (2) demonstrating that by using a prior initial field on th
Externí odkaz:
http://arxiv.org/abs/2305.10460
We propose a direct mesh-free method for performing topology optimization by integrating a density field approximation neural network with a displacement field approximation neural network. We show that this direct integration approach can give compa
Externí odkaz:
http://arxiv.org/abs/2305.04107