AINeutralarXiv – CS AI · 10h ago6/10
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Recovering Physical Dynamics from Discrete Observations via Intrinsic Differential Consistency
Researchers present a novel method for reconstructing continuous-time physical dynamics from discrete observations by enforcing the semi-group property of autonomous flows, using a metric called Symmetry Rupture to regularize training and guide adaptive step selection. The approach significantly outperforms Neural ODE baselines on diffusion-reaction and PDE benchmarks, reducing errors by 87% while requiring 5x fewer function evaluations.