633 citations · 2.9k across the 36 of their papers we have counts for
36 papers
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…
Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components
Alexander Wikner, Jaideep Pathak, Brian R. Hunt +3
We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is in the form of noisy partial measurements of the past and present state…
Combining Machine Learning with Knowledge-Based Modeling for Scalable Forecasting and Subgrid-Scale Closure of Large, Complex, Spatiotemporal Systems
Alexander Wikner, Jaideep Pathak, Brian Hunt +5
We consider the commonly encountered situation (e.g., in weather forecasting) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system…
The Recurrent Processing Unit: Hardware for High Speed Machine Learning
Heidi Komkov, Alessandro Restelli, Brian Hunt +3
Machine learning applications are computationally demanding and power intensive. Hardware acceleration of these software tools is a natural step being explored using various techno…
Separation of Chaotic Signals by Reservoir Computing
Sanjukta Krishnagopal, Michelle Girvan, Edward Ott +1
We demonstrate the utility of machine learning in the separation of superimposed chaotic signals using a technique called Reservoir Computing. We assume no knowledge of the dynamic…
Backpropagation Algorithms and Reservoir Computing in Recurrent Neural Networks for the Forecasting of Complex Spatiotemporal Dynamics
Pantelis R. Vlachas, Jaideep Pathak, Brian R. Hunt +4
We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal dynamics of high dimensional and reduced order complex systems using Reservoir Computing (R…