Short- and long-term prediction of a chaotic flow: A physics-constrained reservoir computing approach

02/15/2021
by   Nguyen Anh Khoa Doan, et al.
0

We propose a physics-constrained machine learning method-based on reservoir computing- to time-accurately predict extreme events and long-term velocity statistics in a model of turbulent shear flow. The method leverages the strengths of two different approaches: empirical modelling based on reservoir computing, which it learns the chaotic dynamics from data only, and physical modelling based on conservation laws, which extrapolates the dynamics when training data becomes unavailable. We show that the combination of the two approaches is able to accurately reproduce the velocity statistics and to predict the occurrence and amplitude of extreme events in a model of self-sustaining process in turbulence. In this flow, the extreme events are abrupt transitions from turbulent to quasi-laminar states, which are deterministic phenomena that cannot be traditionally predicted because of chaos. Furthermore, the physics-constrained machine learning method is shown to be robust with respect to noise. This work opens up new possibilities for synergistically enhancing data-driven methods with physical knowledge for the time-accurate prediction of chaotic flows.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 3

page 7

12/23/2019

A physics-aware machine to predict extreme events in turbulence

We propose a physics-aware machine learning method to time-accurately pr...
12/20/2020

Auto-Encoded Reservoir Computing for Turbulence Learning

We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn...
04/25/2022

Data-driven prediction and control of extreme events in a chaotic flow

An extreme event is a sudden and violent change in the state of a nonlin...
05/23/2018

Machine-learning prediction of fluid variables from data using reservoir computing

We predict both microscopic and macroscopic variables of a chaotic fluid...
03/01/2017

Learning A Physical Long-term Predictor

Evolution has resulted in highly developed abilities in many natural int...
06/17/2021

Gradient-free optimization of chaotic acoustics with reservoir computing

We develop a versatile optimization method, which finds the design param...
12/23/2021

Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models fo...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.