Physics-Informed Neural Networks

Novel transformer-enhanced physics-informed neural network (TE-PINN) achieving 36.8% error reduction in quaternion-based attitude estimation from IMU data. Integrates multi-head attention mechanisms with physics-based constraints (quaternion kinematics, rigid body dynamics) for robust real-time orientation estimation in high-noise, dynamic conditions.

In this tutorial, we will explore Physics Informed Neural Networks (PINNs), which are neural networks trained to solve supervised learning tasks while respecting given laws of physics described by general nonlinear partial differential equations.