Leibniz MMS Days 2024 - Abstract
Dias Ribeiro, Mateus
Computational Fluid Dynamics (CFD) simulation by the numerical solution of the Navier-Stokes equations is an essential tool in a wide range of applications from engineering design to climate modeling. However, the computational cost and memory demand required by CFD codes may become very high for flows of practical interest, such as in aerodynamic shape optimization. This expense is associated with the complexity of the fluid flow governing equations, which include non-linear partial derivative terms that are of difficult solution, leading to long computational times and limiting the number of hypotheses that can be tested during the process of iterative design. Therefore, we propose DeepCFD: a neural network development platform to create models that efficiently approximate solutions for fluid flow problems and other complex engineering regression tasks. We show our latest advances with the incorporation of graph neural networks, and in special physics-informed neural networks.