We present a Bayesian inversion-based digital twin that employs acoustic
pressure data from seafloor sensors, along with 3D coupled acoustic-gravity
wave equations, to infer earthquake-induced spatiotemporal seafloor motion in
real time and forecast tsunami propagation toward coastlines for early warning
with quantified uncertainties. Our target is the Cascadia subduction zone, with
one billion parameters. Computing the posterior mean alone would require 50
years on a 512 GPU machine. Instead, exploiting the shift invariance of the
parameter-to-observable map and devising novel parallel algorithms, we induce a
fast offline-online decomposition. The offline component requires just one
adjoint wave propagation per sensor; using MFEM, we scale this part of the
computation to the full El Capitan system (43,520 GPUs) with 92% weak parallel
efficiency. Moreover, given real-time data, the online component exactly solves
the Bayesian inverse and forecasting problems in 0.2 seconds on a modest GPU
system, a ten-billion-fold speedup.

Cet article explore les excursions dans le temps et leurs implications.

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