NeurIPS 2026 RealPDE Competition: Scientific ML for Real-world Physical Systems

Forecasting the dynamics of complex physical systems is one of the most formidable open challenges across science and engineering, and also serves as a stringent testbed for pushing the frontier of machine learning. Despite the rapid progress of Scientific Machine Learning (Scientific ML), most existing methods are still developed and evaluated primarily on simulated data, which limits their practical deployment due to the substantial gap between simulation and real-world scenario. To squarely tackle this challenge, we present the RealPDE competition designed to advance Scientific ML for real-world applications by bridging the gap between simulation and real world. The competition is built on a new dataset of paired real-world and simulated fluid dynamics trajectories of the NACA4418 airfoil under aligned experimental and numerical configurations. It features two complementary tracks corresponding to practical application scenarios: Simulation-to-Real-world transfer learning, which focuses on bridging their significant gap, and Long-Term Test-Time Adaptation, which studies long-horizon autoregressive prediction with continuous adaptation to streaming real-world observations. To better reflect practical deployment requirements, we introduce a physics-aware and deployment-oriented evaluation protocol that jointly considers physical fidelity, inference efficiency, and prediction reliability through metrics such as turbulent kinetic energy error, mean velocity profile error, normalized runtime, and safe prediction score. As the first NeurIPS Scientific ML competition centered on the gap between simulation and real world for complex physical systems, this competition aims to foster reproducible evaluation and accelerate the development of accurate, robust, efficient, and deployable Scientific ML methods.