Monte Carlo science

Abstract

This paper explores how far the scientific discovery process can be automated. Using the identification of causally significant flow structures in two-dimensional turbulence as an example, it probes how far the usual procedure of planning experiments to test hypotheses can be substituted by ‘blind’ randomised experiments and notes that the increased efficiency of computers is beginning to make such a ‘Monte-Carlo’ approach practical in fluid mechanics. The process of data generation, classification and model creation is described in some detail, stressing the importance of validation and verification. Although the purpose of the paper is to explore the procedure, rather than to model two-dimensional turbulence, it is encouraging that the Monte Carlo process naturally leads to the consideration of vortex dipoles as building blocks of the flow, on par with the more conventional individual vortex cores. Although not completely novel, this ‘spontaneous’ discovery supports the claim that an important advantage of randomised experiments is to bypass researcher prejudice and alleviate paradigm lock. It is finally noted that the method can be extended to three-dimensional flows in practical times.

Keywords:

• Causality
• undirected experiments
• data analysis
• two-dimensional turbulence
• coherent structures
• artificial intelligence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the H2020 European Research Council under the Coturb grant ERC-2014.AdG-669505.