Frequency-compensated PINNs for Fluid-dynamic Design Problems

Understand

Incompressible fluid flow around a cylinder is one of the classical problems in fluid-dynamics with strong relevance with many real-world engineering problems, for example, design of offshore structures or design of a pin-fin heat exchanger.

• Thus learning a high-accuracy surrogate for this problem can demonstrate the efficacy of a novel machine learning approach.
• In this work, we propose a physics-informed neural network (PINN) architecture for learning the relationship between simulation output and the underlying geometry and boundary conditions.
• In addition to using a physics-based regularization term, the proposed approach also exploits the underlying physics to learn a set of Fourier features, i.e.