Thursday, 29 October 2026, 15:00 at Centre for Mathematical Sciences, MR14 – Efficient and robust optimization is essential for training neural networks, enabling scientific machine learning models to converge rapidly to high accuracy while faithfully capturing complex physical behavior governed by partial differential equations (PDEs). In this work, we present advanced optimization strategies to accelerate the convergence of physics-informed neural networks (PINNs) …