Domains using DeepXDE
Physics-Informed Neural Net for Aeroelasticity
Train a neural network that respects the laws of physics
Build a physics-informed neural network (PINN) using DeepXDE to solve aeroelastic flutter equations. Combine deep learning with structural dynamics to predict wing flutter boundaries.
Start Project → AI/MLSolve the Heat Equation with a PINN
Use a neural network to simulate how heat flows through a spacecraft wall.
Use DeepXDE, a Python library for physics-informed neural networks, to solve the 1D heat equation without writing a traditional finite-element solver. Learn how PINNs are changing the way engineers approach thermal analysis in aerospace.
Start Project → AI/MLAirfoil Flow Field with DeepXDE PINNs
Replace a CFD mesh with a neural network trained on physics equations.
Use DeepXDE to build a physics-informed neural network that solves the 2D steady incompressible Euler equations around a NACA 0012 airfoil, predicting pressure and velocity fields without a traditional computational mesh. You will enforce no-penetration boundary conditions on the airfoil surface and validate against panel method results.
Start Project →