Zobrazeno 1 - 10
of 356
pro vyhledávání: '"Shayani P"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Transition metal dichalcogenides (TMDs) are a class of 2D materials demonstrating promising properties, such as high capacities and cycling stabilities, making them strong candidates to replace graphitic anodes in lithium-ion batteries. Howe
Externí odkaz:
https://doaj.org/article/2a8a24cd53254321ae6ce5675533ec13
Autor:
Sanghi, Aditya, Khani, Aliasghar, Reddy, Pradyumna, Rampini, Arianna, Cheung, Derek, Malekshan, Kamal Rahimi, Madan, Kanika, Shayani, Hooman
Large-scale 3D generative models require substantial computational resources yet often fall short in capturing fine details and complex geometries at high resolutions. We attribute this limitation to the inefficiency of current representations, which
Externí odkaz:
http://arxiv.org/abs/2411.08017
Phase evolution and structural modulation during in situ lithiation of MoS2, WS2 and graphite in TEM
Autor:
Chanchal Ghosh, Manish Kumar Singh, Shayani Parida, Matthew T. Janish, Arthur Dobley, Avinash M. Dongare, C. Barry Carter
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
Abstract Li-ion batteries function by Li intercalating into and through the layered electrode materials. Intercalation is a solid-state interaction resulting in the formation of new phases. The new observations presented here reveal that at the nanos
Externí odkaz:
https://doaj.org/article/206b7565ab084791a11524279592b053
Publikováno v:
IDETC 2024
Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather im
Externí odkaz:
http://arxiv.org/abs/2407.11991
Autor:
Hui, Ka-Hei, Sanghi, Aditya, Rampini, Arianna, Malekshan, Kamal Rahimi, Liu, Zhengzhe, Shayani, Hooman, Fu, Chi-Wing
Significant progress has been made in training large generative models for natural language and images. Yet, the advancement of 3D generative models is hindered by their substantial resource demands for training, along with inefficient, non-compact,
Externí odkaz:
http://arxiv.org/abs/2401.11067
Autor:
Sanghi, Aditya, Jayaraman, Pradeep Kumar, Rampini, Arianna, Lambourne, Joseph, Shayani, Hooman, Atherton, Evan, Taghanaki, Saeid Asgari
Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation. This motivates our investigation of how these pre-trained models can be used effecti
Externí odkaz:
http://arxiv.org/abs/2307.03869
Autor:
Sanghi, Aditya, Fu, Rao, Liu, Vivian, Willis, Karl, Shayani, Hooman, Khasahmadi, Amir Hosein, Sridhar, Srinath, Ritchie, Daniel
Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producin
Externí odkaz:
http://arxiv.org/abs/2211.01427
We introduce UNIST, the first deep neural implicit model for general-purpose, unpaired shape-to-shape translation, in both 2D and 3D domains. Our model is built on autoencoding implicit fields, rather than point clouds which represents the state of t
Externí odkaz:
http://arxiv.org/abs/2112.05381
Autor:
Meltzer, Peter, Shayani, Hooman, Khasahmadi, Amir, Jayaraman, Pradeep Kumar, Sanghi, Aditya, Lambourne, Joseph
Boundary Representations (B-Reps) are the industry standard in 3D Computer Aided Design/Manufacturing (CAD/CAM) and industrial design due to their fidelity in representing stylistic details. However, they have been ignored in the 3D style research. E
Externí odkaz:
http://arxiv.org/abs/2105.02961
Autor:
Yu, Fenggen, Chen, Zhiqin, Li, Manyi, Sanghi, Aditya, Shayani, Hooman, Mahdavi-Amiri, Ali, Zhang, Hao
We introduce CAPRI-Net, a neural network for learning compact and interpretable implicit representations of 3D computer-aided design (CAD) models, in the form of adaptive primitive assemblies. Our network takes an input 3D shape that can be provided
Externí odkaz:
http://arxiv.org/abs/2104.05652