PDE-PFN: Prior-Data Fitted Neural PDE Solver
PDE-PFN is a neural solver that uses prior data fitted to approximate solutions of partial differential equations (PDEs). It achieves this by employing in-context learning, allowing the model to infer PDE solutions from given observations without needing explicit operator demonstrations or gradient updates. This method shows strong generalization across different equations and robustness against noisy training data.