Zobrazeno 1 - 10
of 69
pro vyhledávání: '"LIU, BURIGEDE"'
We numerically investigate the hydrodynamic characteristics and analyze the instability mechanism of a two-dimensional inverted flag clamped by a cylinder. Two transition routes and a total of six kinds of solutions exist under this configuration for
Externí odkaz:
http://arxiv.org/abs/2408.10507
The advent of quantum computers, operating on entirely different physical principles and abstractions from those of classical digital computers, sets forth a new computing paradigm that can potentially result in game-changing efficiencies and computa
Externí odkaz:
http://arxiv.org/abs/2312.03791
Autor:
Sun, Xingsheng, Liu, Burigede
This paper concerns the study of optimal (supremum and infimum) uncertainty bounds for systems where the input (or prior) probability measure is only partially/imperfectly known (e.g., with only statistical moments and/or on a coarse topology) rather
Externí odkaz:
http://arxiv.org/abs/2212.14709
This paper concerns the study of history dependent phenomena in heterogeneous materials in a two-scale setting where the material is specified at a fine microscopic scale of heterogeneities that is much smaller than the coarse macroscopic scale of ap
Externí odkaz:
http://arxiv.org/abs/2210.17443
Publikováno v:
Journal of Machine Learning Research (2023) Volume 24, Issue 1, Article No. 388, pp 18593-18618
Deep learning surrogate models have shown promise in solving partial differential equations (PDEs). Among them, the Fourier neural operator (FNO) achieves good accuracy, and is significantly faster compared to numerical solvers, on a variety of PDEs,
Externí odkaz:
http://arxiv.org/abs/2207.05209
Fully resolving dynamics of materials with rapidly-varying features involves expensive fine-scale computations which need to be conducted on macroscopic scales. The theory of homogenization provides an approach to derive effective macroscopic equatio
Externí odkaz:
http://arxiv.org/abs/2205.14139
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 1 December 2024 432 Part B
Autor:
Li, Zongyi, Zheng, Hongkai, Kovachki, Nikola, Jin, David, Chen, Haoxuan, Liu, Burigede, Azizzadenesheli, Kamyar, Anandkumar, Anima
In this paper, we propose physics-informed neural operators (PINO) that combine training data and physics constraints to learn the solution operator of a given family of parametric Partial Differential Equations (PDE). PINO is the first hybrid approa
Externí odkaz:
http://arxiv.org/abs/2111.03794
Autor:
Kovachki, Nikola, Li, Zongyi, Liu, Burigede, Azizzadenesheli, Kamyar, Bhattacharya, Kaushik, Stuart, Andrew, Anandkumar, Anima
Publikováno v:
The Journal of Machine Learning Research (2023), Volume 24, Issue 1, Article No 89, pp 4061-4157
The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets. We propose a generalization of neural networks to learn operators, termed neural operators, that map b
Externí odkaz:
http://arxiv.org/abs/2108.08481
Autor:
Li, Zongyi, Liu-Schiaffini, Miguel, Kovachki, Nikola, Liu, Burigede, Azizzadenesheli, Kamyar, Bhattacharya, Kaushik, Stuart, Andrew, Anandkumar, Anima
Chaotic systems are notoriously challenging to predict because of their sensitivity to perturbations and errors due to time stepping. Despite this unpredictable behavior, for many dissipative systems the statistics of the long term trajectories are g
Externí odkaz:
http://arxiv.org/abs/2106.06898