Mechanism Learning: Prototype-Anchored Mechanism Inference for Scientific Forecasting
Scientific forecasting typically relies on direct state prediction, an approach that grows brittle under data scarcity, extended horizons, non-stationary dynamics, or high-dimensional complexity. While raw state trajectories are highly sensitive in these regimes, underlying local evolution rules often exhibit robust reusability. We introduce mechanism learning, a framework that forecasts future states by estimating the currently active local mechanism. Our method compresses local spatiotemporal fragments into mechanism descriptors, forming a data-driven, structured mechanism space where proximity reflects similar local evolution rules. To ground these estimates in observed data, we utiliz...
| Authors | Qian Jiang, Liping Sun |
|---|---|
| Primary category | cs.LG |
| Published | 16 May 2026 |
| Last updated at source | 16 May 2026 |
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