12:00–1:00 pm
GCIS E123 929 E. 57th Street
Machine learning (ML) has the potential to revolutionize science by uncovering new physical laws governing complex systems. In recent years, ML has been successfully used to infer parsimonious equations describing simulated complex systems where the underlying physics is known. However, very few new laws of physics have been inferred from experimental data. Here we demonstrate an ML model capable of extracting physical forces from systems of many particles with complex interactions. The model is trained on experimental data: micron-sized particles immersed in weakly-ionized plasma (dusty plasma, DP). A vast array of collective, emergent behaviors have been reported in DP, but the particle charge, interaction law, and plasma properties are difficult to measure in-situ. Using laser-sheet tomography, we track the 3D positions of 10-20 particles in various plasma environments over minutes. The symmetries that govern our dusty plasma system have been built-in to the ML model, and we use neural networks to represent the particle interaction and environmental forces. The model achieves 99% of the variance when predicting the acceleration from experimental particle trajectories, and successfully identifies non-reciprocal interactions between particles, the particle mass, and the particle charge. We also demonstrate how the model fails in certain regimes where N-body interactions govern the force between particles. Finally, we expect the model can be easily tailored to describe other many-body systems where high-quality experimental data is available.