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Learning Stochastic Dynamics from Data
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Learning Stochastic Dynamics from Data
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We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm.
Forward citations
Cited by 2 Pith papers
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A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
The Weak Penalty Neural ODE uses a weak form loss to filter noise and learn stable chaotic dynamics from noisy observations.
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A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series
Adding a weak-form penalty to the standard pointwise loss makes Neural ODE training robust to observation noise and preserves long-term invariant statistics on chaotic benchmarks and ERA5 climate data.
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