Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE

Talk Title: Starter-Iterator Neural Operator: A Unified Architecture for High-Fidelity Forward and Inverse PDE Problems Speaker: Prof. Yuping Duan Date: August 25th, 2026 Abstract: Operator learning provides an efficient surrogate framework for solving high-dimensional partial differential equations, particularly in many-query applications such as real-time prediction and parameter studies. However, existing methods often face accuracy limitations when handling complex boundaries, long-term dynamics, and inverse problems. In this talk, I will introduce the Starter-Iterator Neural Operator (SINO), which reformulates the initialization and iterative refinement strategies of classical numerical methods within a neural-operator framework. Its frequency-domain starter captures globally stable features, while its time-domain iterator progressively reduces local solution residuals. Experiments on the Navier–Stokes equations, acoustic wave equations, super-resolution imaging, and weather forecasting demonstrate strong accuracy, generalization, and robustness.