Numerical integration schemes used in optimizing models of dynamical systems from sampled data can distort learned stability properties, inducing anti-damping artifacts in originally damped systems.
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PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.
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Artifacts of Numerical Integration in Learning Dynamical Systems
Numerical integration schemes used in optimizing models of dynamical systems from sampled data can distort learned stability properties, inducing anti-damping artifacts in originally damped systems.
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Physically Interpretable World Models via Weakly Supervised Representation Learning
PIWM aligns latent states in image-based world models with physical variables and constrains their dynamics to known equations via weak distribution supervision, yielding accurate long-horizon predictions and parameter recovery on Cart Pole, Lunar Lander, and Donkey Car.