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Found 4 papers, 3 papers with code
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimizing the drag force exerted on an object in a fluid flow.
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
In this paper, we provide a unifying theoretical and methodological framework for incorporating symmetry into machine learning models in three ways: 1. enforcing known symmetry when training a model; 2. discovering unknown symmetries of a given model or data set; and 3. promoting symmetry during training by learning a model that breaks symmetries within a user-specified group of candidates when there is sufficient evidence in the data.
Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not.
Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body
In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not.
