From the 2 of 5 linked papers with an AI index.
5 papers
Comparison of a Parametric Physics-Informed Neural Network and a Tensorial Reduced-Order Model for the Shallow-Water Dam-Break Problem
Anton Myshak, Md Rezwan Bin Mizan, Ilya Timofeyev
The paper develops and compares two parametric data-driven reduced models—a physics-informed neural network and a tensorial reduced-order model—for the one-dimensional shallow-wate…
Subgrid-Scale Parameterization in Burgers' Equation Using Structure-Preserving Neural Networks and Entropy Variables
Aijaz Nazir, Ilya Timofeyev
The paper introduces a machine‑learning method that uses structure‑preserving neural networks and entropy variables to create subgrid‑scale parameterizations for coarse simulations…
Reduced-Order Modeling of Parameterized Visco-Plastic Shallow Flows
Md Rezwan Bin Mizan, Ilya Timofeyev, Maxim Olshanskii
We propose a non-intrusive reduced-order modeling framework for parametrized visco-plastic free-surface flows governed by a shallow-water formulation of Herschel--Bulkley fluids. T…
Parametrization of subgrid scales in long-term simulations of the shallow-water equations using machine learning and convex limiting
Md Amran Hossan Mojamder, Zhihang Xu, Min Wang +1
We present a method for parametrizing sub-grid processes in the Shallow Water equations. We define coarse variables and local spatial averages and use a feed-forward neural network…
A parametric tensor ROM for the shallow water dam break problem
Md Rezwan Bin Mizan, Maxim Olshanskii, Ilya Timofeyev
We develop a variant of a tensor reduced-order model (tROM) for the parameterized shallow-water dam-break problem. This hyperbolic system presents multiple challenges for model red…