Zobrazeno 1 - 6
of 6
pro vyhledávání: '"George, Robert Joseph"'
Autor:
Kumarappan, Adarsh, Tiwari, Mo, Song, Peiyang, George, Robert Joseph, Xiao, Chaowei, Anandkumar, Anima
Large Language Models (LLMs) have been successful in mathematical reasoning tasks such as formal theorem proving when integrated with interactive proof assistants like Lean. Existing approaches involve training or fine-tuning an LLM on a specific dat
Externí odkaz:
http://arxiv.org/abs/2410.06209
Autor:
Rahman, Md Ashiqur, George, Robert Joseph, Elleithy, Mogab, Leibovici, Daniel, Li, Zongyi, Bonev, Boris, White, Colin, Berner, Julius, Yeh, Raymond A., Kossaifi, Jean, Azizzadenesheli, Kamyar, Anandkumar, Anima
Existing neural operator architectures face challenges when solving multiphysics problems with coupled partial differential equations (PDEs) due to complex geometries, interactions between physical variables, and the limited amounts of high-resolutio
Externí odkaz:
http://arxiv.org/abs/2403.12553
This paper proposes a practical and efficient solution for computing convolutions using hybrid dealiasing. It offers an alternative to explicit or implicit dealiasing and includes an optimized hyperparameter tuning algorithm that uses experience to f
Externí odkaz:
http://arxiv.org/abs/2306.10016
Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows. At the core of FNO is a spectral layer that leverages a discretization-convergent representation in the Fo
Externí odkaz:
http://arxiv.org/abs/2211.15188
Autor:
George, Robert Joseph, Yu, Xinwei
The utilization of renewable energy technologies, particularly hydrogen, has seen a boom in interest and has spread throughout the world. Ethanol steam reformation is one of the primary methods capable of producing hydrogen efficiently and reliably.
Externí odkaz:
http://arxiv.org/abs/2210.13745
Recently, neural networks have proven their impressive ability to solve partial differential equations (PDEs). Among them, Fourier neural operator (FNO) has shown success in learning solution operators for highly non-linear problems such as turbulenc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7de78554ab92c04ef470df1b29f6c02d
http://arxiv.org/abs/2211.15188
http://arxiv.org/abs/2211.15188