<p>In this episode of <strong>podcast_v0.1</strong>, we break down the groundbreaking paper <em>&quot;Real-time Bayesian inference at extreme scale: A digital twin for tsunami early warning applied to the Cascadia subduction zone.&quot;</em> Imagine shrinking a 50-year supercomputer job into 0.2 seconds of computation on a regular GPU—that’s exactly what these researchers achieved. We explore how they used offline/online decomposition, extreme-scale simulations, and Bayesian inference to create a real-time tsunami forecasting system capable of saving lives. You&#39;ll learn about the clever use of shift invariance, the role of uncertainty quantification, and how computational design—not just brute force—can redefine what&#39;s possible. This is a must-listen if you&#39;re interested in high-performance computing, real-world digital twins, or how engineering innovation solves critical, time-sensitive problems.</p><p><br>Read the original paper: <a href="http://arxiv.org/abs/2504.16344v1" target="_blank" rel="ugc noopener noreferrer">http://arxiv.org/abs/2504.16344v1</a></p><p>Music: &#39;The Insider - A Difficult Subject&#39;</p>