3 papers
cs.LG2026
Curvature-aware dynamic precision approach for physics-informed neural networks
Yingjie Shao, Ioannis N. Athanasiadis, George van Voorn +1
Physics-informed neural networks (PINNs) have become a promising framework for simulating partial differential equations (PDEs) by embedding physical laws directly into neural netw…
cs.LG2026
Late Fusion Neural Operators for Extrapolation Across Parameter Space in Partial Differential Equations
Eva van Tegelen, Taniya Kapoor, George A. K. van Voorn +2
Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter regimes is crucial for robust…
cs.LG2025
Neural Ordinary Differential Equations for Learning and Extrapolating System Dynamics Across Bifurcations
Eva van Tegelen, George van Voorn, Ioannis Athanasiadis +1
Forecasting system behaviour near and across bifurcations is crucial for identifying potential shifts in dynamical systems. While machine learning has recently been used to learn c…