research
∙
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...
share
research
∙
10/07/2021
A Meta-learning Approach to Reservoir Computing: Time Series Prediction with Limited Data
Recent research has established the effectiveness of machine learning fo...
research
∙
08/27/2021
Parallel Machine Learning for Forecasting the Dynamics of Complex Networks
Forecasting the dynamics of large complex networks from previous time-se...
research
∙
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...
research
∙
10/27/2020
Hybrid Backpropagation Parallel Reservoir Networks
In many real-world applications, fully-differentiable RNNs such as LSTMs...
research
∙
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...
research
∙
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...
research
∙
06/12/2019
Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks
Parkinson's disease (PD) is a common neurodegenerative disease with a hi...
research
∙
01/25/2019
Computational landscape of user behavior on social media
With the increasing abundance of 'digital footprints' left by human inte...
research
∙
03/09/2018
Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model
A model-based approach to forecasting chaotic dynamical systems utilizes...
research
∙
06/26/2013