21 January 2026
Recent advances in machine learning have begun to embed oscillatory network principles within neural architectures, aiming to enhance computational efficiency and robustness in time-series regression.
Building on these developments, EMERGE partners from the Delft University of Technology take a step toward applying such principles to the learning of physical dynamics. This study introduces Neural Linear Oscillator Networks (nLON): a vision-based framework that extracts compact latent representations of complex motion directly from image sequences.
Read the paper in the link below.
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