Kernel method for corrections to scaling

Understand

Scaling analysis, in which one infers scaling exponents and a scaling function in a scaling law from given data, is a powerful tool for determining universal properties of critical phenomena in many fields of science.

• However, there are corrections to scaling in many cases, and then the inference problem becomes ill-posed by an uncontrollable irrelevant scaling variable.
• We propose a new kernel method based on Gaussian process regression to fix this problem generally.
• We test the performance of the new kernel method for some example cases.