供水管网决策共享一个水力梯度，且现在可以精确计算
Water-network decisions share one hydraulic gradient, and it can now be computed exactly
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中文总结 AI 辅助
该研究使供水管网全局梯度算法精确可微，通过伴随求解一次获得所有参数梯度，显著加速校准与泄漏定位，并在52个网络上验证了高精度和效率优势。
中文摘要 AI 辅助
供水管网（WDN）上的校准、泄漏定位和传感器布置是关于连续参数的决策，然而定义物理过程的水力引擎返回解而不提供导数，因此实践中不得不依赖无导数搜索或代理模型，而代理模型的误差会传递给最终答案。我们使全局梯度算法本身精确可微：前向传播复现参考引擎的离散设备、状态切换和低流量线性化，反向传播通过复用前向传播的终端分解来求解隐式伴随方程，因此一次额外的稀疏求解即可同时返回每个参数的梯度，并在单个图形处理器上按场景批量处理。在52个公共、合成和实际运行管网及8,140个模拟帧中，每个网络均满足验收标准，与EPANET 2.2的最大水头偏差为1.137e-13英尺，其中25个网络完全一致。一次伴随求解替代了在905管道的L-TOWN基准上有限差分粗糙度雅可比矩阵所需的906次模拟，而泄漏反演训练循环在256个场景下每个优化器步骤运行时间为463-470毫秒，是先前流程的191倍。梯度校准在中位595次模型调用内达到终点，而五个调优元启发式方法中最强的一个需要8,060次才能在训练损失上匹配它，且有两个在20,000次内从未达到。在一个554条管道的运行网络上，一次伴随传递逐管道审计了已安装传感器能够约束哪些粗糙度参数以及应添加哪些传感器，基于公用事业公司已运行的模型。
英文摘要
Calibration, leak localisation and sensor placement on water distribution networks (WDNs) are decisions about continuous parameters, yet the hydraulic engine that defines the physics returns a solution and no derivatives, so practice falls back on derivative-free search or on surrogates whose error the answer inherits. We make the global gradient algorithm itself exactly differentiable: the forward pass reproduces the reference engine's discrete devices, status switching and low-flow linearisation included, and the backward pass solves the implicit adjoint by reusing the forward pass's terminal factorisation, so one extra sparse solve returns every parameter's gradient at once, batched over scenarios on one graphics processor. Across 52 public, synthetic and operational networks and 8,140 simulation frames, every network meets the acceptance criterion, the largest head deviation from EPANET 2.2 is 1.137e-13 ft and 25 agree exactly. One adjoint solve replaces the 906 simulations a finite-difference roughness Jacobian costs on the 905-pipe L-TOWN benchmark, and a leak-inversion training loop runs at 463-470 ms per optimiser step for 256 scenarios, 191 times the prior pipeline. Gradient calibration reaches its endpoint within a median 595 model calls, where the strongest of five tuned metaheuristics needs 8,060 to match it on the training loss and two never do within 20,000. On a 554-link operating network, one adjoint pass audits, pipe by pipe, which roughness parameters the installed sensors can constrain and which sensors to add, on the model the utility already operates.
发表机构
- Guangzhou Institute of Industrial Intelligence（广州工业智能研究院）
- Key Laboratory of Ecological Restoration of Regional Contaminated Environment, Ministry of Education, College of Environment, Shenyang University（沈阳大学环境学院教育部区域污染生态环境恢复重点实验室）
- School of Municipal Engineering and Environment, Shenyang Jianzhu University（沈阳建筑市政与环境工程学院）
- Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所）
- College of Environmental Science and Engineering, Donghua University（东华大学环境科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
