Sensor Placement for Tsunami Early Warning via Large-Scale Bayesian Optimal Experimental Design
2026-04-09 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster Computing
AI summaryⓘ
The authors developed a way to improve how we set up sensor networks used for real-time tsunami warnings by using a method called Bayesian optimal experimental design (OED). Because tsunami prediction involves complex equations that are tough to handle, they created a new scalable approach that works well with many GPUs (powerful computers). Their method turns the problem into selecting the best subsets of data, and they tested it on a digital tsunami model with 175 sensors, greatly reducing uncertainty in a huge amount of data. This approach helps make tsunami early warning systems more accurate and efficient.
Bayesian optimal experimental designhyperbolic partial differential equationslinear time-invariant systemssensor networksinverse problemsdigital twinmulti-GPU computingCascadia Subduction Zoneschur complement
Authors
Sreeram Venkat, Stefan Henneking, Omar Ghattas
Abstract
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.