Adaptive Learning and Prediction of Multiscale Dynamical Systems

Author:

Kičić, Ivica [claim]

Description:

Computer simulations are an indispensable tool for analyzing complex, multiscale systems in a wide range of scientific and engineering disciplines. In many such systems, the governing dynamics occur at the micro scale, whereas quantities of interest manifest themselves on orders of magnitude larger macro scale. To reduce the computational cost, the simulations are ideally performed directly on the macro and not the micro scale. However, finding an effective macro-scale model that reflects well its micro-scale counterpart remains a key challenge for Computational Science. In this thesis, we apply machine learning algorithms to automatically identify a system's macro-scale representation, effective macro-scale dynamics and the transfer of information between micro and macro scales. We present Adaptive Learning of Effective Dynamics (AdaLED), a framework for building online macro-scale surrogate models of complex multiscale systems based on autoencoders and ensembles of recurrent neural networks. The framework trains the surrogate on the fly and continuously monitors its prediction accuracy. Depending on the accuracy, it automatically switches the control of the simulation between the micro and macro scale dynamics to achieve a balance between speed-up and accuracy. Compared to other approaches, AdaLED enables for the first time the creation of adaptive surrogate models with online accuracy estimation that operate on the level of a time step. AdaLED models allow us to accelerate parts of dynamics that are simpler to learn while leaving the more complex ones to the micro-scale dynamics. The thesis further touches on the problem of coupling known micro and macro models and handling open boundary conditions for particle dynamics, used in particle-continuum coupling in multiscale fluid dynamics. We propose ideas for generalizing existing algorithms, limited to static cubical and spherical domains, to complex time-varying geometries. In particular, we show the generalization of the method for cross-boundary mass flow ...

Publisher:

ETH Zurich

Contributors:

Koumoutsakos, Petros ; Pezzè, Mauro ; Zavadlav, Julija

Year of Publication:

2022

Document Type:

info:eu-repo/semantics/doctoralThesis ; [Doctoral and postdoctoral thesis]

Language:

Subjects:

machine learning ; surrogate modeling ; Software design ; molecular dynamics ; Coupled models ; info:eu-repo/classification/ddc/004 ; info:eu-repo/classification/ddc/600 ; Data processing ; computer science ; Technology (applied sciences)

DDC:

004 Data processing & computer science

Rights:

info:eu-repo/semantics/openAccess ; http://creativecommons.org/licenses/by/4.0/ ; Creative Commons Attribution 4.0 International

Terms of Re-use:

CC-BY

Relations:

http://hdl.handle.net/20.500.11850/600553

URL:

https://hdl.handle.net/20.500.11850/600553
https://doi.org/10.3929/ethz-b-000600553

Content Provider:

ETH Zürich Research Collection