Our work serves as a practical guideline for machine learning practitioners by comparing several popular network architectures in terms of accuracy and ...

Jul 13, 2023 · In this paper, we successfully demonstrate the application of physics-informed neural networks for modeling steady and transient flows through ...

Jan 3, 2022 · This paper studies the capability of PINN for solving the Navier-Stokes equations in two formulations (velocity-pressure and the Cauchy stress tensor)

Jun 30, 2024 · We therefore present a parameterized surrogate model for the prediction of three-dimensional flow fields in aerothermal vehicle simulations.

The physics-informed neural network (PINN) method is extended to learn and predict compressible steady-state aerodynamic flows with a high Reynolds number. To ...

May 24, 2024 · In this study, we compare three network architectures to optimize the multi-case PINN through experiments on a series of idealized 2D stenotic ...

Simulation of Steady and Transient 3D Flows via Physics-Informed Deep Learning · Current and emerging deep-learning methods for the simulation of fluid dynamics.

Mar 22, 2024 · The physics-informed neural network (PINN) method is extended to learn and predict compressible steady-state aerodynamic flows with a high Reynolds number.

A recurrent neural network based model, which learns representations of long-term spatial-temporal dependencies in the sequence of its input data.

We present a novel physics-informed deep learning framework for solving steady-state incompressible flow on multiple sets of irregular geometries by ...