用深度学习实现稀疏传感器数据下的快速高精度流场重建
BLISSNet: Deep Operator Learning for Fast and Accurate Flow Reconstruction from Sparse Sensor Measurements
- 基于DeepONet架构，支持任意尺寸域的零样本推理
- 首次调用后预计算部分网络，后续推理速度超越传统插值方法
- 兼顾高精度与低计算成本，适合大规模实时流场重建
从稀疏传感器测量中重构流场是科学与工程中的基础挑战。由于测量点间距大且流动具有复杂的多尺度特性，精确恢复细粒度结构非常困难。现有方法普遍存在权衡：高精度模型计算开销大，而快速方法往往牺牲保真度。本文提出BLISSNet，一种在流场重建与基于助推的数据同化中兼具高精度与高效性的模型。该模型采用类似DeepONet的架构，支持对任意尺寸域的零样本推理。首次在特定域上调用后，部分网络组件可预计算，使后续在大域上的评估推理成本极低。因此，其推理速度优于径向基函数或双三次插值等经典方法。高精度、低成本与零样本泛化能力的结合，使BLISSNet非常适合大规模实时流场重建与数据同化任务。
原文摘要 · Abstract (English)
Reconstructing fluid flows from sparse sensor measurements is a fundamental challenge in science and engineering. Widely separated measurements and complex, multiscale dynamics make accurate recovery of fine-scale structures difficult. In addition, existing methods face a persistent tradeoff: high-accuracy models are often computationally expensive, whereas faster approaches typically compromise fidelity. In this work, we introduce BLISSNet, a model that strikes a strong balance between reconstruction accuracy and computational efficiency for both flow reconstruction and nudging-based data assimilation. The model follows a DeepONet-like architecture, enabling zero-shot inference on domains of arbitrary size. After the first model call on a given domain, certain network components can be precomputed, leading to low inference cost for subsequent evaluations on large domains. Consequently, the model can achieve faster inference than classical interpolation methods such as radial basis function or bicubic interpolation. This combination of high accuracy, low cost, and zero-shot generalization makes BLISSNet well-suited for large-scale real-time flow reconstruction and data assimilation tasks.
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