Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
Abstract
A critical challenge in the data-driven modeling of dynamical systems is producing methods robust to measurement error, particularly when data is limited. Many leading methods either rely on denoising prior to learning or on access to large volumes of data to average over the effect of noise. We propose a novel paradigm for data-driven modeling that simultaneously learns the dynamics and estimates the measurement noise at each observation. By...
Paper Details
Title
Deep learning of dynamics and signal-noise decomposition with time-stepping constraints
Published Date
Nov 1, 2019
Volume
396
Pages
483 - 506
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