Behind-the-Meter Energy Hub Dispatch for Data Centers

Mon, 01 Jan 0001 00:00:00 +0000

Problem

AI workloads can push a data center’s power demand up by tens of megawatts in minutes, often faster than its grid connection can supply. On-site gas turbines can cover the gap, but they respond over minutes while computing load changes in seconds. Keeping the two in balance without tripping any plant limit is a hard control problem.

Approach

Each side of the facility gets its own learned world model: one for the turbine fleet and one for the AI data hall. A single planner looks ahead across both at once and decides how far the turbines should ramp and how much work the data hall can safely defer. Every command is checked against the turbines’ physical limits before it reaches the plant, so the learned components never act unchecked.

Unmanned Aerial Vehicle Safe Autonomous Operations

Mon, 01 Jan 0001 00:00:00 +0000

Problem

Air taxis and delivery drones are expected to fly autonomously over cities, but public trust is low: in one survey cited in this work, only 26% of respondents trusted autonomous air taxis. Before these vehicles can fly on their own, their controllers need a clear safety guarantee: the set of states from which the vehicle can still avoid a collision or reach its landing pad. Computing that set means solving a Hamilton-Jacobi equation, and grid-based solvers become impractical for realistic vehicle models with 6 to 12 states. Learning-based controllers scale better, but they are hard to certify.

Physics-Informed-Ml on Omanshu Thapliyal

Behind-the-Meter Energy Hub Dispatch for Data Centers

Problem

Approach

Unmanned Aerial Vehicle Safe Autonomous Operations

Problem