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[HTML][HTML] Energy-conserving neural network for turbulence closure modeling
In turbulence modeling, we are concerned with finding closure models that represent the
effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards …
effect of the subgrid scales on the resolved scales. Recent approaches gravitate towards …
An extended neural ordinary differential equation network with grey system and its applications
F Zhang, X **ao, M Gao - Neurocomputing, 2024 - Elsevier
The neural ordinary differential equation (NODE) has attracted much attention for its
applicability in dynamic system modeling and continuous time series analysis. When the …
applicability in dynamic system modeling and continuous time series analysis. When the …
Physics-constrained coupled neural differential equations for one dimensional blood flow modeling
Background: Computational cardiovascular flow modeling plays a crucial role in
understanding blood flow dynamics. While 3D models provide acute details, they are …
understanding blood flow dynamics. While 3D models provide acute details, they are …
a priori uncertainty quantification of reacting turbulence closure models using Bayesian neural networks
G Pash, M Hassanaly, S Yellapantula - Engineering Applications of …, 2025 - Elsevier
While many physics-based closure model forms have been posited for the sub-filter scale
(SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical …
(SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical …
Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models
Stably stratified turbulence (SST), a model that is representative of the turbulence found in
the oceans and atmosphere, is strongly affected by fine balances between forces and …
the oceans and atmosphere, is strongly affected by fine balances between forces and …
Enhancing Low-Order Discontinuous Galerkin Methods with Neural Ordinary Differential Equations for Compressible Navier--Stokes Equations
S Kang, EM Constantinescu - arxiv preprint arxiv:2310.18897, 2023 - arxiv.org
The growing computing power over the years has enabled simulations to become more
complex and accurate. While immensely valuable for scientific discovery and problem …
complex and accurate. While immensely valuable for scientific discovery and problem …