针对复杂混沌系统预测，提出自适应池化计算框架，显著提升多场景泛化能力。
Adaptive Reservoir Computing for Multi-Scenario Chaotic System Forecasting
- 按不同任务需求动态调整回声网络训练与推理策略
- 在12项任务中取得74.91分，优于通用方法
- 适用于小样本、噪声干扰、参数变化等复杂场景
我们提出一种自适应的储层计算框架，用于CFT-4-Science Lorenz基准测试，该测试涵盖12个任务，覆盖五类不同性质的场景：基础预测、含噪信号重建、带噪声预测、少样本学习和参数泛化。不同于使用统一推理策略，我们根据每种评估场景的具体需求，定制回声状态网络（ESNs）的训练与预测流程。主要贡献包括：(1) 精确的储层状态同步，消除短时预测中的预热近似误差；(2) 基于直方图的候选选择机制，直接优化长期遍历性评价指标；(3) 多种子储层搜索，适用于训练数据严重受限的少样本情形；(4) 顺序多序列训练，解决参数泛化任务中的状态分布不匹配问题。所提框架在公开排行榜上获得74.91分，表明精心设计的自适应储层计算是一种高效且具有竞争力的混沌系统建模方法。
原文摘要 · Abstract (English)
We present an adaptive reservoir computing framework for the CTF-4-Science Lorenz benchmark, which evaluates machine learning models across twelve distinct tasks spanning five qualitatively different scenarios: baseline forecasting, noisy signal reconstruction, forecasting under noise, few-shot learning, and parametric generalization. Rather than applying a uniform inference strategy, we tailor the training and prediction procedure of Echo State Networks (ESNs) to the specific demands of each evaluation scenario. Our key contributions are fourfold: (1) exact reservoir state synchronization that eliminates warmup approximation error in short-time prediction; (2) histogram-guided candidate selection that directly optimizes the long-time ergodic evaluation metric; (3) multi-seed reservoir search for few-shot regimes with severely limited training data; and (4) sequential multi-sequence training that resolves state-distribution mismatch in parametric generalization tasks. The proposed framework achieves a score of 74.91 on the public benchmark leaderboard, demonstrating that carefully adapted reservoir computing constitutes a competitive and computationally efficient approach for diverse chaotic system modeling challenges.
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