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11/09/2022
Stabilizing Machine Learning Prediction of Dynamics: Noise and Noise-inspired Regularization
Recent work has shown that machine learning (ML) models can be trained t...
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07/01/2022
Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems
In this paper we consider the machine learning (ML) task of predicting t...
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08/27/2021
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Forecasting the dynamics of large complex networks from previous time-se...
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02/15/2021
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
We consider the problem of data-assisted forecasting of chaotic dynamica...
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10/29/2020
Link inference of noisy delay-coupled networks: Machine learning and opto-electronic experimental tests
We devise a machine learning technique to solve the general problem of i...
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02/10/2020
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
We consider the commonly encountered situation (e.g., in weather forecas...
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12/05/2019
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links
We introduce and test a general machine-learning-based technique for the...
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10/09/2019
Forecasting of Spatio-temporal Chaotic Dynamics with Recurrent Neural Networks: a comparative study of Reservoir Computing and Backpropagation Algorithms
How effective are Recurrent Neural Networks (RNNs) in forecasting the sp...
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03/09/2018
