GarNet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping
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In this thesis, we advocate that Computer-Aided Engineering could benefit from a Geometric Deep Learning revolution, similarly to the way that Deep Learning revolutionized Computer Vision. To do so, we consider a variety of Computer-Aided Engineering pro ...
In recent years, there has been a significant revolution in the field of deep learning, which has demonstrated its effectiveness in automatically capturing intricate patterns from large datasets. However, the majority of these successes in Computer Vision ...
Curvilinear structures are frequently observed in a variety of domains and are essential for comprehending neural circuits, detecting fractures in materials, and determining road and irrigation canal networks. It can be costly and time-consuming to manuall ...
Surrogate deep neural networks (DNNs) can significantly speed up the engineering design process by providing a quick prediction that emulates simulated data. Many previous works have considered improving the accuracy of such models by introducing additiona ...
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling. To alleviate this, we introduce a pipeline for data-driven simulation ...
This thesis consists of three applications of machine learning techniques to empirical asset pricing.In the first part, which is co-authored work with Oksana Bashchenko, we develop a new method that detects jumps nonparametrically in financial time series ...
Vahidi, Dara, and Fernando Porté-Agel. "A physics-based model for wind turbine wake expansion in the atmospheric boundary layer." Journal of Fluid Mechanics 943 (2022). ...
Current post-earthquake damage assessment methodologies are not only time-consuming but also subjective in nature and difficult to document. Recent advancements in artificial intelligence and technological devices make it possible to accomplish this task a ...
Deployable gridshells are a class of planar-to-spatial structures that achievea 3D curved geometry by inducing bending on a flat grid of elastic beams. However, theslender nature of these beams often conflicts with the structure’s load-bearing capacity.To ...
Deep image denoisers achieve state-of-the-art results but with a hidden cost. As witnessed in recent literature, these deep networks are capable of overfitting their training distributions, causing inaccurate hallucinations to be added to the output and ge ...
Society for Imaging Science and Technology (IS&T)2022