Zobrazeno 1 - 10
of 99
pro vyhledávání: '"Kossaifi, Jean"'
Autor:
Siddiqui, Shoaib Ahmed, Kossaifi, Jean, Bonev, Boris, Choy, Christopher, Kautz, Jan, Krueger, David, Azizzadenesheli, Kamyar
Despite tremendous progress in developing deep-learning-based weather forecasting systems, their design space, including the impact of different design choices, is yet to be well understood. This paper aims to fill this knowledge gap by systematicall
Externí odkaz:
http://arxiv.org/abs/2410.07472
Autor:
Shah, Freya, Patti, Taylor L., Berner, Julius, Tolooshams, Bahareh, Kossaifi, Jean, Anandkumar, Anima
Fourier Neural Operators (FNOs) excel on tasks using functional data, such as those originating from partial differential equations. Such characteristics render them an effective approach for simulating the time evolution of quantum wavefunctions, wh
Externí odkaz:
http://arxiv.org/abs/2409.03302
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 lack of large amounts of high-re
Externí odkaz:
http://arxiv.org/abs/2403.12553
Autor:
Xu, Minkai, Han, Jiaqi, Lou, Aaron, Kossaifi, Jean, Ramanathan, Arvind, Azizzadenesheli, Kamyar, Leskovec, Jure, Ermon, Stefano, Anandkumar, Anima
Modeling the complex three-dimensional (3D) dynamics of relational systems is an important problem in the natural sciences, with applications ranging from molecular simulations to particle mechanics. Machine learning methods have achieved good succes
Externí odkaz:
http://arxiv.org/abs/2401.11037
Memory complexity and data scarcity have so far prohibited learning solution operators of partial differential equations (PDEs) at high resolutions. We address these limitations by introducing a new data efficient and highly parallelizable operator l
Externí odkaz:
http://arxiv.org/abs/2310.00120
Autor:
Azizzadenesheli, Kamyar, Kovachki, Nikola, Li, Zongyi, Liu-Schiaffini, Miguel, Kossaifi, Jean, Anandkumar, Anima
Scientific discovery and engineering design are currently limited by the time and cost of physical experiments, selected mostly through trial-and-error and intuition that require deep domain expertise. Numerical simulations present an alternative to
Externí odkaz:
http://arxiv.org/abs/2309.15325
Autor:
Li, Zongyi, Kovachki, Nikola Borislavov, Choy, Chris, Li, Boyi, Kossaifi, Jean, Otta, Shourya Prakash, Nabian, Mohammad Amin, Stadler, Maximilian, Hundt, Christian, Azizzadenesheli, Kamyar, Anandkumar, Anima
We propose the geometry-informed neural operator (GINO), a highly efficient approach to learning the solution operator of large-scale partial differential equations with varying geometries. GINO uses a signed distance function and point-cloud represe
Externí odkaz:
http://arxiv.org/abs/2309.00583
Autor:
Tu, Renbo, White, Colin, Kossaifi, Jean, Bonev, Boris, Kovachki, Nikola, Pekhimenko, Gennady, Azizzadenesheli, Kamyar, Anandkumar, Anima
Neural operators, such as Fourier Neural Operators (FNO), form a principled approach for learning solution operators for PDEs and other mappings between function spaces. However, many real-world problems require high-resolution training data, and the
Externí odkaz:
http://arxiv.org/abs/2307.15034
Autor:
Lim, Jae Hyun, Kovachki, Nikola B., Baptista, Ricardo, Beckham, Christopher, Azizzadenesheli, Kamyar, Kossaifi, Jean, Voleti, Vikram, Song, Jiaming, Kreis, Karsten, Kautz, Jan, Pal, Christopher, Vahdat, Arash, Anandkumar, Anima
Diffusion models have recently emerged as a powerful framework for generative modeling. They consist of a forward process that perturbs input data with Gaussian white noise and a reverse process that learns a score function to generate samples by den
Externí odkaz:
http://arxiv.org/abs/2302.07400
Transformers have attained superior performance in natural language processing and computer vision. Their self-attention and feedforward layers are overparameterized, limiting inference speed and energy efficiency. Tensor decomposition is a promising
Externí odkaz:
http://arxiv.org/abs/2211.16749