Surrogate modeling of multi-dimensional premixed and non-premixed combustion using pseudo-time stepping physics-informed neural networks
Physics-informed neural networks (PINNs) have emerged as a promising alternative to
conventional computational fluid dynamics (CFD) approaches for solving and modeling …
conventional computational fluid dynamics (CFD) approaches for solving and modeling …
Physics-informed neural networks with domain decomposition for the incompressible Navier–Stokes equations
Physics-informed neural network (PINN) has emerged as a promising approach for solving
differential equations in recent years. However, their application to large-scale complex …
differential equations in recent years. However, their application to large-scale complex …
A Parameterized Prediction Method for Turbulent Jet Noise based on Physics-informed Neural Networks
Turbulent noise prediction is integral to fluid equipment design, and multiple simulations or
experiments are often required for noise distribution under varying operating conditions …
experiments are often required for noise distribution under varying operating conditions …