Physics-constrained machine learning strategies for turbulent flows and bubble dynamics

Machine learning (ML) has in recent years become a sizzling trend in almost every science and engineering discipline. It enables scientists and engineers to make decisions or draw conclusions directly using information extracted from data, bypassing the necessity to unravel the delicate inner workings of the underlying phenomena. This, however, comes at the expense of having to search through an immense space of potential architectures and parameters for an optimized model that, not only provides the best description to the available data, but also applies to unseen cases. To cope with such difficulties, it is imperative that ample constraints are imposed on the architecture and parameter space, in order to facilitate efficient and generalizable learning. For physical systems, first-principle knowledge makes up a natural set of constraints that should be integrated into the ML system.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Wan, Zhong Yi,Ph. D.Massachusetts Institute of Technology.

Advisor dc:contributor.advisor

• Themistoklis P. Sapsis.

• Mechanical Engineering.

Statement dc:rights

• MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/127061

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/127061