ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks

Understand

The loss functions of many learning problems contain multiple additive terms that can disagree and yield conflicting update directions.

• For Physics-Informed Neural Networks (PINNs), loss terms on initial/boundary conditions and physics equations are particularly interesting as they are well-established as highly difficult tasks.
• To improve learning the challenging multi-objective task posed by PINNs, we propose the ConFIG method, which provides conflict-free updates by ensuring a positive dot product between the final update and each loss-specific gradient.
• It also maintains consistent optimization rates for all loss terms and dynamically adjusts gradient magnitudes based on conflict levels.