A research team at The University of Texas at Austin has been awarded the prestigious 2025 Gordon Bell Prize for their groundbreaking work on real-time tsunami forecasting. Led by Omar Ghattas, a professor of mechanical engineering and principal faculty member at the Oden Institute, the team received this honor on November 20 during the SC25 conference, a leading event in high-performance computing.
The Gordon Bell Prize, often referred to as the Nobel Prize of supercomputing, recognizes significant advancements in high-performance computing applications across various scientific fields. The team’s innovative approach has the potential to significantly enhance early warning systems for coastal communities vulnerable to earthquakes and tsunamis. By integrating pressure sensor data from the seafloor with predictive physics-based models, they created a digital twin capable of forecasting tsunami propagation.
Revolutionary Digital Twin Algorithms
One of the most remarkable aspects of this research is the team’s new digital twin algorithms, which achieve a staggering 10-billion-fold speedup over existing methods. What previously required 50 years of supercomputing time can now be completed in mere seconds. This rapid processing capability is crucial for timely decision-making, potentially saving countless lives.
“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,”
said Ghattas, project lead.
The research primarily focused on the seismically active Cascadia subduction zone in the Pacific Northwest, a region identified by seismologists as having a 37% probability of experiencing a magnitude 8.0 or higher earthquake within the next 50 years.
The team utilized some of the world’s most powerful supercomputers, including Lawrence Livermore National Laboratory’s El Capitan, the National Energy Research Scientific Computing Center’s Perlmutter, and others. This collaboration allowed them to solve complex problems involving over 50 trillion unknowns on more than 40,000 GPUs, achieving remarkable parallel efficiency.
Future Applications and Broader Impact
The implications of this research extend beyond tsunami forecasting. The digital twin framework developed by the team could adapt to other hazards, including wildfires, severe weather, and contaminant spread, enhancing predictive capabilities in various fields.
“In combining the power of El Capitan with advanced high-order finite element algorithms, we were able to tackle unprecedented challenges in computational mathematics,” said Tzanio Kolev, a computational mathematician at Lawrence Livermore National Laboratory.
Additionally, the digital twin framework employs seafloor pressure data alongside 3D coupled acoustic-gravity wave equations. Utilizing novel Bayesian inversion algorithms, the team can infer earthquake-induced motion in the seafloor and simulate tsunami propagation with high precision. This process allows for the issuance of tsunami forecasts that include quantified uncertainties, aiding evacuation efforts and informing first responders before tsunamis reach coastal areas, which can occur within 15 minutes of an earthquake.
The research paper detailing their findings, titled “Real-Time Bayesian Inference at Extreme Scale: A Digital Twin for Tsunami Early Warning Applied to the Cascadia Subduction Zone,” was published as part of the SC25 conference proceedings on November 15. In addition to the Gordon Bell Prize, the team received two other accolades at SC25: the Hyperion Research HPC Innovation Excellence Award and the HPCWire Reader’s Choice Award for Best Use of HPC in Physical Sciences.
The Gordon Bell Prize, named after the influential computer architect who helped establish the U.S. National Science Foundation’s Directorate for Computer and Information Science and Engineering, is awarded annually to recognize outstanding contributions in high-performance computing. This latest achievement underscores the transformative potential of advanced computing technologies in addressing critical global challenges.
