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Linear Transformations in Autoencoder Latent Space Predict Time Translations in Active Matter System
ML approaches are relevant in active matter, a field that spans scales and studies dynamics of far-from-equilibrium systems where there are significant challenges in predicting macroscopic behavior from microscopic interactions of active particles.
Reinforcement Learning reveals fundamental limits on the mixing of active particles
The control of far-from-equilibrium physical systems, including active materials, has emerged as an important area for the application of reinforcement learning (RL) strategies to derive control policies for physical systems.
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