[2603.23799] Resolving gradient pathology in physics-informed epidemiological models

Computer Science > Machine Learning arXiv:2603.23799 (cs) [Submitted on 25 Mar 2026] Title:Resolving gradient pathology in physics-informed epidemiological models Authors:Nickson Golooba, Woldegebriel Assefa Woldegerima View a PDF of the paper titled Resolving gradient pathology in physics-informed epidemiological models, by Nickson Golooba and Woldegebriel Assefa Woldegerima View PDF HTML (experimental) Abstract:Physics-informed neural networks (PINNs) are increasingly used in mathematical epidemiology to bridge the gap between noisy clinical data and compartmental models, such as the susceptible-exposed-infected-removed (SEIR) model. However, training these hybrid networks is often unstable due to competing optimization objectives. As established in recent literature on ``gradient pathology," the gradient vectors derived from the data loss and the physical residual often point in conflicting directions, leading to slow convergence or optimization deadlock. While existing methods attempt to resolve this by balancing gradient magnitudes or projecting conflicting vectors, we propose a novel method, conflict-gated gradient scaling (CGGS), to address gradient conflicts in physics-informed neural networks for epidemiological modelling, ensuring stable and efficient training and a computationally efficient alternative. This method utilizes the cosine similarity between the data and physics gradients to dynamically modulate the penalty weight. Unlike standard annealing schemes t...