对于从事地球科学模拟或物理仿真的研究者，COGENT 解决了不规则网格上长期预测不稳定的痛点，且支持任意时间点查询，值得在气候建模或工程仿真中尝试。
COGENT 是一种结合图神经网络和神经常微分方程（Neural ODE）的连续时间仿真器，专为不规则地理空间网格上的长期物理预测设计。它通过图编码器处理历史系统状态和外部强迫，生成上下文向量来初始化潜在神经ODE，从而在连续时间域中预测未来状态。该模型支持任意时间点的查询，无需逐步反馈预测状态，显著提升了长期预测的稳定性。在冰盖模拟任务中，COGENT 优于传统自回归图模型，展示了其在需要稳定长时预测场景中的潜力。
COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT encodes a finite history of system states and associated forcing fields and external forcings with a graph-based history encoder, producing node-wise context vectors that capture both local spatial interactions and temporal evolution. These context vectors initialize and condition a latent Neural Ordinary Differential Equation whose dynamics are driven by interpolated future forcings and explicit relative rollout time. By modeling the forecast trajectory as a continuous latent dynamical system, COGENT can generate predictions at arbitrary future times rather than being restricted to a fixed temporal discretization. A residual decoder maps the resulting latent trajectories back to future physical states, enabling direct multi-step forecasting without repeatedly feeding predicted states back into the model. This formulation combines graph-based spatial representation, history-conditioned latent dynamics, and continuous-time rollout in a unified framework for mesh-based physical simulation emulation. In order to stabilize training with long-horizon supervision, we also propose effective rollout-horizon sampling and a progressive rollout-horizon scheduling strategy. We evaluate COGENT on transient ice-sheet simulations generated by the Ice-sheet and Sea-level System Model, demonstrating improved long-range stability over autoregressive graph baselines. These results suggest that continuous graph Neural ODEs provide a promising methodology for scalable physical forecasting on irregular geospatial meshes, particularly in applications that require stable long-horizon predictions and the ability to query system states at arbitrary times.