Table 2 Comparison of Active Subspaces, PyDimension, BuckiNet, and BSM across validation and application cases: Rayleigh problem, Colebrook equation, Malkus waterwheel, Rayleight-Bénard convection, Blasius boundary layer, Velocity scaling, Wall flux, Skin friction, MHD generator, Laser-metal interaction

From: Dimensionless learning based on information

Method

Active

PyDim-

Bucki-

BSM

IT-π

 

Subspaces

ension

Net

 

(Current)

Rayleigh problem

Colebrook equation

× 

× 

Malkus waterwheel

× 

× 

× 

× 

Rayleight-Bénard convection

× 

× 

× 

Blasius boundary layer

Velocity scaling

72%

21%

50%

N/A

12%

Wall shear stress

78%

54%

74%

N/A

19%

Wall heat flux

62%

44%

37%

N/A

9%

Skin friction

19%

27%

75%

N/A

17%

MHD generator

7%

7%

98%

N/A

5%

Laser-metal interaction

94%

25%

100%

N/A

23%

  1. For the validation cases, the table presents whether the methods could identify the correct dimensionless variables for validations cases ( or  × ). For the application cases, the table shows the normalized irreducible error \({\tilde{\epsilon }}_{LB}\) associated to the dimensionless input and output variables identified by each method with lower values indicating better performance.