This work introduces the Nirenberg Neural Network: a numerical approach to the Nirenberg problem of prescribing Gaussian curvature on S2 for metrics that are pointwise conformal to the round metric. Our mesh-free physics-informed neural network (PINN) approach directly parametrises the conformal factor globally and is trained with a geometry-aware loss enforcing the curvature equation. Additional consistency checks were performed via the Gauss-Bonnet theorem, and spherical-harmonic expansions were fit to the learnt models to provide interpretability. For prescribed curvatures with known realisability, the neural network achieves very low losses (10−7−10−10), while unrealisable curvatures yield significantly higher losses. This distinction enables the assessment of unknown cases, separating likely realisable functions from non-realisable ones. The current capabilities of the Nirenberg Neural Network demonstrate that neural solvers can serve as exploratory tools in geometric analysis, offering a quantitative computational perspective on longstanding existence questions.
@article{arxiv.2602.12368,
title = {A Machine Learning Approach to the Nirenberg Problem},
author = {Gianfranco Cortés and Maria Esteban-Casadevall and Yueqing Feng and Jonas Henkel and Edward Hirst and Tancredi Schettini Gherardini and Alexander G. Stapleton},
journal= {arXiv preprint arXiv:2602.12368},
year = {2026}
}