Learning from History: A Retrieval-Augmented Framework for Spatiotemporal Prediction

TL;DR

This paper introduces a Retrieval-Augmented Prediction framework that combines deep learning with historical data retrieval to improve long-term spatiotemporal predictions, ensuring more physically realistic results.

Contribution

The novel RAP framework integrates historical analog retrieval with deep models, enhancing long-term prediction accuracy and physical plausibility in complex systems.

Findings

Outperforms state-of-the-art methods in meteorology, turbulence, and fire simulation.

Significantly reduces error accumulation in long-term predictions.

More physically realistic trajectories compared to existing approaches.

Abstract

Accurate and long-term spatiotemporal prediction for complex physical systems remains a fundamental challenge in scientific computing. While deep learning models, as powerful parametric approximators, have shown remarkable success, they suffer from a critical limitation: the accumulation of errors during long-term autoregressive rollouts often leads to physically implausible artifacts. This deficiency arises from their purely parametric nature, which struggles to capture the full constraints of a system's intrinsic dynamics. To address this, we introduce a novel \textbf{Retrieval-Augmented Prediction (RAP)} framework, a hybrid paradigm that synergizes the predictive power of deep networks with the grounded truth of historical data. The core philosophy of RAP is to leverage historical evolutionary exemplars as a non-parametric estimate of the system's local dynamics. For any given state, RAP efficiently retrieves the most similar historical analog from a large-scale database. The true future evolution of this analog then serves as a \textbf{reference target}. Critically, this target is not a hard constraint in the loss function but rather a powerful conditional input to a specialized dual-stream architecture. It provides strong \textbf{dynamic guidance}, steering the model's predictions towards physically viable trajectories. In extensive benchmarks across meteorology, turbulence, and fire simulation, RAP not only surpasses state-of-the-art methods but also significantly outperforms a strong \textbf{analog-only forecasting baseline}. More importantly, RAP generates predictions that are more physically realistic by effectively suppressing error divergence in long-term rollouts.