<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Cyberphysical-Systems on Omanshu Thapliyal</title><link>https://omanshuthapliyal.github.io/tags/cyberphysical-systems/</link><description>Recent content in Cyberphysical-Systems on Omanshu Thapliyal</description><generator>Hugo</generator><language>en</language><atom:link href="https://omanshuthapliyal.github.io/tags/cyberphysical-systems/index.xml" rel="self" type="application/rss+xml"/><item><title>Safety and Cybersecurity of Cyberphysical Systems</title><link>https://omanshuthapliyal.github.io/projects/safety-and-cybersecurity-of-cyberphysical-systems/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://omanshuthapliyal.github.io/projects/safety-and-cybersecurity-of-cyberphysical-systems/</guid><description>&lt;h2 id="problem">Problem&lt;/h2>
&lt;p>Drones, air taxis and robot teams are cyberphysical systems: physical machines
run by software and linked by networks. Before they can be trusted in
safety-critical airspace, we need to know every state they could reach, and
whether any of those states is unsafe. The exact answer comes from solving
high-dimensional partial differential equations, which is out of reach once a
system is nonlinear, learned from data by a neural network, spread across many
agents, or under cyberattack.&lt;/p></description></item></channel></rss>