Distinguishing Dynamical Kinds: An Approach for Automating Scientific Discovery

The automation of scientific discovery has been an active research topic for many years. The promise of a formalized approach to developing and testing scientific hypotheses has attracted researchers from the sciences, machine learning, and philosophy alike. Leveraging the concept of dynamical symmetries a new paradigm is proposed for the collection of scientific knowledge, and algorithms are presented for the development of EUGENE – an automated scientific discovery tool-set. These algorithms have direct applications in model validation, time series analysis, and system identification. Further, the EUGENE tool-set provides a novel metric of dynamical similarity that would allow a system to be clustered into its dynamical regimes. This dynamical distance is sensitive to the presence of chaos, effective order, and nonlinearity. I discuss the history and background of these algorithms, provide examples of their behavior, and present their use for exploring system dynamics.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Science and Applications

Department dc:contributor.department

Computer Science

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2019

Author dc:creator

• Shea-Blymyer, Colin

Chair dc:contributor.committeechair

• Jantzen, Benjamin C.

Committee members dc:contributor.committeemember

• Huang, Bert
• Karpatne, Anuj
• Prakash, B. Aditya

• Data Analysis
• Dynamical Kinds
• Nonlinear Systems
• Chaos
• Automated Scientific Discovery
• Order Identification

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:21401

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/101659