Real-time tsunami early warning relies on distributed sensor networks to infer seismic sources and seafloor motion. Optimizing these networks via Bayesian optimal experimental design (OED) is exceptionally challenging for systems governed by hyperbolic partial differential equations, which lack the spectral decay required by standard low-rank approximations. We present a scalable Bayesian OED framework for linear time-invariant systems. By reformulating the inverse problem in the data space, we transform OED into dense matrix subset selection. We propose a multi-GPU, Schur-complement-update-based, greedy algorithm that solves the OED problem using a pipelined approach that fully overlaps I/O with GPU computations. Our framework achieves near-perfect weak and strong scaling across hundreds of GPUs on Perlmutter and Frontier. Applied to the 2025 Gordon Bell Prize-winning digital twin for tsunami forecasting in the Cascadia Subduction Zone, we optimize a 175-sensor network, minimizing the uncertainty of a parameter field with over one billion degrees of freedom.

翻译：实时海啸预警依赖于分布式传感器网络来推断震源及海底运动。通过贝叶斯最优实验设计优化这些网络，对于受双曲型偏微分方程支配的系统而言极具挑战，因为此类系统缺乏标准低秩近似所需的谱衰减特性。我们针对线性时不变系统提出了一种可扩展的贝叶斯最优实验设计框架。通过将反问题重新表述为数据空间形式，我们将最优实验设计转化为稠密矩阵子集选择问题。我们提出了一种基于多GPU、舒尔补更新策略的贪婪算法，采用流水线方式求解最优实验设计问题，该方式可完全重叠输入/输出操作与GPU计算。我们的框架在Perlmutter和Frontier系统的数百个GPU上实现了近乎完美的弱扩展和强扩展。将其应用于荣获戈登贝尔奖的2025年卡斯卡迪亚俯冲带海啸预报数字孪生系统，我们优化了一个由175个传感器构成的网络，将拥有超过十亿自由度的参数场不确定性降至最低。