Zobrazeno 1 - 10
of 85
pro vyhledávání: '"P. A. Bryngelson"'
Autor:
Wilfong, Benjamin, Radhakrishnan, Anand, Berre, Henry A. Le, Abbott, Steve, Budiardja, Reuben D., Bryngelson, Spencer H.
GPUs are the heart of the latest generations of supercomputers. We efficiently accelerate a compressible multiphase flow solver via OpenACC on NVIDIA and AMD Instinct GPUs. Optimization is accomplished by specifying the directive clauses 'gang vector
Externí odkaz:
http://arxiv.org/abs/2409.10729
Autor:
Shahane, Shantanu, Chammas, Sheide, Bezgin, Deniz A., Buhendwa, Aaron B., Schmidt, Steffen J., Adams, Nikolaus A., Bryngelson, Spencer H., Chen, Yi-Fan, Wang, Qing, Sha, Fei, Zepeda-Núñez, Leonardo
Conventional WENO3 methods are known to be highly dissipative at lower resolutions, introducing significant errors in the pre-asymptotic regime. In this paper, we employ a rational neural network to accurately estimate the local smoothness of the sol
Externí odkaz:
http://arxiv.org/abs/2409.09217
An optimal sequential experimental design approach is developed to computationally characterize soft material properties at the high strain rates associated with bubble cavitation. The approach involves optimal design and model inference. The optimal
Externí odkaz:
http://arxiv.org/abs/2409.00011
Partial differential equation solvers are required to solve the Navier-Stokes equations for fluid flow. Recently, algorithms have been proposed to simulate fluid dynamics on quantum computers. Fault-tolerant quantum devices might enable exponential s
Externí odkaz:
http://arxiv.org/abs/2406.00280
Publikováno v:
Transactions on Machine Learning Research; ISSN 2835-8856 (2024); https://openreview.net/forum?id=z9SIj-IM7tn
We demonstrate that neural networks can be FLOP-efficient integrators of one-dimensional oscillatory integrands. We train a feed-forward neural network to compute integrals of highly oscillatory 1D functions. The training set is a parametric combinat
Externí odkaz:
http://arxiv.org/abs/2404.05938
Computational fluid dynamics (CFD) simulations often entail a large computational burden on classical computers. At present, these simulations can require up to trillions of grid points and millions of time steps. To reduce costs, novel architectures
Externí odkaz:
http://arxiv.org/abs/2401.12248
Publikováno v:
Physical Review Fluids, 9, 094606 (2024)
Reynolds-averaged Navier--Stokes (RANS) closure must be sensitive to the flow physics, including nonlocality and anisotropy of the effective eddy viscosity. Recent approaches used forced direct numerical simulations to probe these effects, including
Externí odkaz:
http://arxiv.org/abs/2310.08763
Autor:
Bati, Ajay, Bryngelson, Spencer H.
Publikováno v:
Computer Physics Communications, 296, 109052 (2024)
The rise of neural network-based machine learning ushered in high-level libraries, including TensorFlow and PyTorch, to support their functionality. Computational fluid dynamics (CFD) researchers have benefited from this trend and produced powerful n
Externí odkaz:
http://arxiv.org/abs/2307.16322
Autor:
Radhakrishnan, Anand, Berre, Henry Le, Wilfong, Benjamin, Spratt, Jean-Sebastien, Rodriguez Jr., Mauro, Colonius, Tim, Bryngelson, Spencer H.
Publikováno v:
Computer Physics Communications (2024), Volume 302, pg 109238
Multiphase compressible flows are often characterized by a broad range of space and time scales. Thus entailing large grids and small time steps, simulations of these flows on CPU-based clusters can thus take several wall-clock days. Offloading the c
Externí odkaz:
http://arxiv.org/abs/2305.09163
Autor:
Kocherla, Sriharsha, Song, Zhixin, Chrit, Fatima Ezahra, Gard, Bryan, Dumitrescu, Eugene F., Alexeev, Alexander, Bryngelson, Spencer H.
Publikováno v:
AVS Quantum Science, 6, 033806 (2024)
Fluid flow simulations marshal our most powerful computational resources. In many cases, even this is not enough. Quantum computers provide an opportunity to speed up traditional algorithms for flow simulations. We show that lattice-based mesoscale n
Externí odkaz:
http://arxiv.org/abs/2305.07148