<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/48bc894db05a4a29aab04cda1255e070&quot; frameborder=&quot;0&quot; width=&quot;2560&quot; height=&quot;1920&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1920</height><width>2560</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1920</thumbnail_height><thumbnail_width>2560</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/48bc894db05a4a29aab04cda1255e070-1702650329481.gif</thumbnail_url><duration>1617</duration><title>ICML22 - Physics AI ABM</title><description>AI-driven multiphysics solvers, based on multi-agent reinforcement learning (MARL).  This video from an invited talk at ICML 2022, and a longer version is available from Cambridge ML journal club: https://drive.google.com/file/d/1EsdkEN1htCZsUSv2asPf90lvoQTWE9V8/view?usp=drive_link</description></oembed>