Every year, companies and space agencies launch hundreds of rockets into space – and that number is set to grow dramatically with ambitious missions to the Moon, Mars and beyond. But these dreams hinge on one critical challenge: propulsion – the methods used to push rockets and spacecraft forward.
To make interplanetary travel faster, safer and more efficient, scientists need breakthroughs in propulsion technology. Artificial intelligence is one type of technology that has begun to provide some of these necessary breakthroughs.
We’re a team of engineers and graduate students who are studying how AI in general, and a subset of AI called machine learning in particular, can transform spacecraft propulsion. From optimizing nuclear thermal engines to managing complex plasma confinement in fusion systems, AI is reshaping propulsion design and operations. It is quickly becoming an indispensable partner in humankind’s journey to the stars.
Machine learning and reinforcement learning
Machine learning is a branch of AI that identifies patterns in data that it has not explicitly been trained on. It is a vast field with its own branches, with a lot of applications. Each branch emulates intelligence in different ways: by recognizing patterns, parsing and generating language, or learning from experience. This last subset in particular, commonly known as reinforcement learning, teaches machines to perform their tasks by rating their performance, enabling them to continuously improve through experience.
As a simple example, imagine a chess player. The player does not calculate every move but rather recognizes patterns from playing a thousand matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a computational speed and scale impossible for humans. It learns through experiences and iterations by observing its environment. These observations allows the machine to correctly interpret each outcome and deploy the best strategies for the system to reach its goal.
Reinforcement learning can improve human understanding of deeply complex systems – those that challenge the limits of human intuition. It can help determine the most efficient trajectory for a spacecraft heading anywhere in space, and it does so by optimizing the propulsion necessary to send the craft there. It can also potentially design better propulsion systems, from selecting the best materials to coming up with configurations that transfer heat between parts in the engine more efficiently.
In reinforcement learning, you can train an AI model to complete tasks that are too complex for humans to complete themselves.
Reinforcement learning for propulsion systems
In regard to space propulsion, reinforcement learning generally falls into two categories: those that assist during the design phase – when engineers define mission needs and system capabilities – and…
