Commutativity regularization mitigates transient error amplification in autoregressive neural simulators by penalizing non-normality and non-commutativity of Jacobians, yielding stable long-horizon rollouts.
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A constrained hypothesis-class framework for identifying mesoscopic dynamics from data, backed by uniform well-posedness and stability guarantees derived from a generalized Onsager principle.
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Controlling Transient Amplification Improves Long-horizon Rollouts
Commutativity regularization mitigates transient error amplification in autoregressive neural simulators by penalizing non-normality and non-commutativity of Jacobians, yielding stable long-horizon rollouts.
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Hypothesis-driven construction of mesoscopic dynamics
A constrained hypothesis-class framework for identifying mesoscopic dynamics from data, backed by uniform well-posedness and stability guarantees derived from a generalized Onsager principle.