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20182026
most citedHybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model

310 citations · 419 across the 8 of their papers we have counts for

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Showing 2022Show all

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cs.LG2022★ 2 cited

Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization

Alexander Wikner, Joseph Harvey, Michelle Girvan +4

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state…