发表机构
Pacific Northwest National Laboratory（西北太平洋国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究受果蝇感知机制启发，提出基于分类的回归新框架，用局部模式库替代全局模型，通过相似性加权重构预测，可降低计算存储需求并控制精度与成本的权衡，适用于非线性系统等场景。
AI 中文摘要
我们提出了一种受果蝇感知环境机制启发的、基于分类的回归任务新方法。具体而言，我们构建了一个通用框架，用于学习非线性输入输出关系，该框架用有限的代表性局部模式库替代复杂的全局代理模型。由于科学数据通常分布在输入空间中有限且重复的区域，我们通过测量查询与存储模式的相似性生成预测，再通过加权重构整合相关响应。我们将该方法应用于非线性动力学系统、数据驱动回归及物理信息学习，采用了合适的嵌入和相似性度量。对于动力学系统，我们的离线-在线工作流在离线阶段从数据或控制方程中提取模式，在线预测仅需相似性评估和响应聚合。该结构有助于降低计算和内存需求，同时可显式控制精度、存储与推理成本之间的权衡。
英文摘要
We present a novel approach to regression tasks using classification which is motivated by the mechanism used by fruitflies to sense their environment. Specifically, we formulate a general framework for learning nonlinear input-output relationships by replacing complex global surrogate models with a finite library of representative local patterns. Since scientific data often occupy limited and recurring regions of the input space, we generate predictions by measuring similarities between a query and stored patterns, then combining their associated responses through weighted reconstruction. We apply this approach to nonlinear dynamical systems, data-driven regression, and physics-informed learning using suitable embeddings and similarity measures. For dynamical systems, our offline-online workflow extracts patterns from data or governing equations during the offline phase, while online prediction requires only similarity evaluation and response aggregation. This structure helps us reduce computational and memory demands while providing explicit control over the trade-off among accuracy, storage, and inference cost.
