Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations. (arXiv:2206.06817v1 [physics.flu-dyn])

Since the derivation of the Navier Stokes equations, it has become possible
to numerically solve real world viscous flow problems (computational fluid
dynamics (CFD)). However, despite the rapid advancements in the performance of
central processing units (CPUs), the computational cost of simulating transient
flows with extremely small time/grid scale physics is still unrealistic. In
recent years, machine learning (ML) technology has received significant
attention across industries, and this big wave has propagated various interests
in the fluid dynamics community. Recent …

arxiv flow learning physics simulations strategy transfer transfer learning