When astrophysicists want to study the universe, they can't just observe everything directly. Many celestial events and processes take millions of years to unfold and happen on scales that are simply too vast. To tackle this, scientists create simulations – specifically, gravitational N-body simulations. These simulations allow them to model the interactions of large numbers of stars or galaxies under the influence of gravity.
Astrophysicists often are after simulations that are as close to reality as possible. However, assessing the realism of these simulations isn't straightforward. Much of the time, it boils down to subjective judgment – which is unreliable and prone to bias.
Moreover, when you start a simulation, you need to set up the initial conditions very precisely. It's like starting a massive, complex video game where you need to decide every detail of the world before you press play. Right now, there's no quick and easy way to get these initial conditions just right without resorting to extremely complex and resource-intensive methods.
Finally, deciding on which simulations to run is a bit like choosing what experiments to perform in a lab. You have a ton of options, but you need to pick the ones that will tell you the most about the system you're interested in. Currently, this decision-making is based on educated guesswork, which isn't always the most efficient.
These issues can lead to simulations that are not as effective or accurate as they could be. This inefficiency has real-world consequences. High-performance computers used to run these simulations consume vast amounts of electricity, so running unnecessary simulations is wasteful.
The RISING project is divided into three main parts, addressing these issues one by one by means of suitable machine learning tools. These are at the core the same generative AI technology that powers AI art and chatbots, applied to simulations instead.
1. RISING::Realism – Here, I am developing new, objective ways to measure how realistic our simulations are. This involves creating tools that can compare the simulated universe with what we observe through telescopes. To do this I leverage deep learning tools, in particular anomaly detection performed by a dedicated generative adversarial network.
2. RISING::Genesis – This is about devising new methods for setting initial conditions without directly relying on hydro-simulations. The tools I am using come from machine learning: the goal of the game is to learn the probability distribution of positions, velocities, and masses of stars coming out of hydrodynamical simulations of star formation to obtain new realisation without the need to to rerun the original simulations.
3. RISING::Active – In this part, I use active learning, which is an intelligent way to automate the process of choosing which simulations to run. Instead of relying on guesswork, I use algorithms that learn from previous simulations and systematically guide us towards the most informative and useful ones.
Through the RISING project I am pushing the boundaries of what we know about the universe by making our simulations more precise, more efficient, smarter, and greener.
