Scientists at Lawrence Livermore National Laboratory have helped develop a real-time tsunami forecasting system that could improve early warning capabilities for coastal communities near earthquake zones. The system is powered by El Capitan, the world’s fastest supercomputer.
With a theoretical peak performance of 2.79 quintillion calculations per second, El Capitan is part of a new generation of exascale supercomputers, designed for solving the most complex problems in science. It was developed through funding from the Advanced Simulation and Computing (ASC) program at the National Nuclear Security Administration (NNSA).
LLNL researchers used El Capitan to create a large library of physics-based simulations that model how seafloor motion from earthquakes generates tsunami waves. The work was detailed in a paper selected as a finalist for the 2025 ACM Gordon Bell Prize, a prestigious award in high-performance computing, according to a press release from LLNL.
The team used over 43,500 AMD Instinct Accelerated Processing Units (APUs) to simulate acoustic-gravity wave propagation, which shows how underwater earthquake waves move through the ocean. By performing this computing in advance, they developed a dataset that smaller computers can later use for rapid, real-time tsunami forecasting.
As part of the project, the researchers developed a tsunami “digital twin” in collaboration with the Oden Institute at the University of Texas at Austin and the Scripps Institution of Oceanography at the University of California, San Diego. This real-time simulation acts as a virtual replica of how the ocean responds to earthquakes.
“This is the first digital twin with this level of complexity that runs in real time,” said LLNL computational mathematician Tzanio Kolev in the press release. “It combines extreme-scale forward simulation with advanced statistical methods to extract physics-based predictions from sensor data at unprecedented speed.”
By leveraging LLNL’s Hewlett Packard Enterprise/AMD supercomputer El Capitan, the team solved a billion-parameter Bayesian inverse problem in less than 0.2 seconds, accurately predicting tsunami wave heights at a speed about 10 billion times faster than existing methods. According to researchers, this capability could significantly improve emergency response and save lives.
“This work is important because it shows that we can solve an inverse problem of enormous size — not for 10 or 15 variables, but for millions, or even billions of variables, very quickly,” Kolev said. “In the past, you’d either have a fast model that’s not accurate, or a full-physics model that takes hours or days. Now we’re showing that we can do both — accurate and fast — using principled mathematics and modern computing.”
At the core of the tsunami forecasting system is MFEM, an open-source simulation software developed by LLNL. Designed to run GPU-accelerated simulations of physical phenomena, MFEM performed the most compute-intensive phase of the project by solving complex equations that link movements on the ocean floor to data from seafloor sensors. This process involved simulating 55.5 trillion variables, and set a new record for the largest simulation of its kind.
“MFEM’s high-order methods and GPU readiness, developed under the ASC program at LLNL and the Department of Energy’s (DOE) Exascale Computing Project, made it possible to scale to the full machine,” Kolev said. “This was really a first-of-its-kind demonstration of how we can use that power not just for raw performance, but also for mission-relevant, time-critical decisions in many MFEM-based applications.”
This advancement paves the way for faster, more reliable tsunami forecasting.
“Conventional tsunami warning systems often … use simplistic models that fail to capture the complexity of fault ruptures, which can lead to false alarms or dangerously late warnings,” officials wrote in the press release. “The team’s approach, instead, uses data from seafloor pressure sensors and solves a full-physics model of acoustic–gravity wave propagation in the ocean in record time.”
As seafloor sensor networks become more widespread along earthquake-prone coasts and computational infrastructure continues to improve, the team sees a clear path to deploying the approach in future tsunami warning systems.
“This framework represents a paradigm shift in how we think about early warning systems,” said Omar Ghattas, professor of mechanical engineering and principal faculty in the Oden Institute at UT Austin.
“For the first time, we can combine real-time sensor data with full-physics modeling and uncertainty quantification — fast enough to make decisions before a tsunami reaches the shore. It opens the door to truly predictive, physics-informed emergency response systems across a range of natural hazards,” Ghattas added.
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