Zobrazeno 1 - 10
of 43
pro vyhledávání: '"MacArt, Jonathan F."'
Trained neural networks (NN) have attractive features for closing governing equations, but in the absence of additional constraints, they can stray from physical reality. A NN formulation is introduced to preclude spurious oscillations that violate s
Externí odkaz:
http://arxiv.org/abs/2408.03413
The neural ordinary differential equation (ODE) framework has shown promise in developing accelerated surrogate models for complex systems described by partial differential equations (PDEs). In PDE-based systems, neural ODE strategies use a two-step
Externí odkaz:
http://arxiv.org/abs/2403.02224
In this study, we introduce a domain-decomposition-based distributed training and inference approach for message-passing neural networks (MPNN). Our objective is to address the challenge of scaling edge-based graph neural networks as the number of no
Externí odkaz:
http://arxiv.org/abs/2402.15106
Autor:
Nista, Ludovico, Schumann, Christoph David Karl, Bode, Mathis, Grenga, Temistocle, MacArt, Jonathan F., Attili, Antonio, Pitsch, Heinz
Publikováno v:
Phys. Rev. Fluids 9 (6), 2024, 064601
Supervised super-resolution deep convolutional neural networks (CNNs) have gained significant attention for their potential in reconstructing velocity and scalar fields in turbulent flows. Despite their popularity, CNNs currently lack the ability to
Externí odkaz:
http://arxiv.org/abs/2308.16015
Autor:
Liu, Xuemin, MacArt, Jonathan F.
We develop neural-network active flow controllers using a deep learning PDE augmentation method (DPM). The sensitivities for optimization are computed using adjoints of the governing equations without restriction on the terms that may appear in the o
Externí odkaz:
http://arxiv.org/abs/2307.09980
The predictive accuracy of the Navier-Stokes equations is known to degrade at the limits of the continuum assumption, thereby necessitating expensive and often highly approximate solutions to the Boltzmann equation. While tractable in one spatial dim
Externí odkaz:
http://arxiv.org/abs/2303.12114
Autor:
Sirignano, Justin, MacArt, Jonathan F.
Deep learning (DL) has recently emerged as a candidate for closure modeling of large-eddy simulation (LES) of turbulent flows. High-fidelity training data is typically limited: it is computationally costly (or even impossible) to numerically generate
Externí odkaz:
http://arxiv.org/abs/2303.02338
Autor:
Sirignano, Justin, MacArt, Jonathan F.
A deep learning (DL) closure model for large-eddy simulation (LES) is developed and evaluated for incompressible flows around a rectangular cylinder at moderate Reynolds numbers. Near-wall flow simulation remains a central challenge in aerodynamic mo
Externí odkaz:
http://arxiv.org/abs/2208.03498
Publikováno v:
Physical Review Fluids 6 (2021) 050502
The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses in a temporally developing plane turbulent jet at Reynolds number $Re_0=6\,000$. The objective fu
Externí odkaz:
http://arxiv.org/abs/2105.01030
Machine learning for scientific applications faces the challenge of limited data. We propose a framework that leverages a priori known physics to reduce overfitting when training on relatively small datasets. A deep neural network is embedded in a pa
Externí odkaz:
http://arxiv.org/abs/1911.09145