Scalable online nonlinear goal-oriented inference with physics-informed maps

This thesis develops a physics-informed k-nearest neighbors approach, which draws from both physics-based modeling and data-driven machine learning. In doing so, our method achieves robustness and increased accuracy with small datasets, while being cheap to apply. Our method tackles the challenges of high-dimensional inverse problems governed by complex physical models. Such inverse problems arise in important engineering applications, such as heat transfer, medical and structural imaging, and contaminant control. In particular, we consider the goal-oriented inverse problem setting, where unknown model parameters are inferred from observations in order to calculate some low-dimensional quantity of interest (QoI). When computational resources and/or time are limited, it is infeasible to solve the full inverse problem for inferred parameters to obtain the QoI.

Name thesis:degree_name

Doctoral

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Aeronautics and Astronautics

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Li, Harriet.

Advisor dc:contributor.advisor

• Karen Willcox.

• Aeronautics and Astronautics.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

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/1721.1/122370

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/122370