The Blankert Operator is a novel mathematical tool that redefines how we measure similarity between functions or datasets. By introducing a tunable parameter α, it captures not only overall alignment but also the nuanced ways in which two signals differ. This makes it vastly more sensitive to nonlinear discrepancies than traditional measures like inner products or Pearson correlation.

Whether you’re analyzing time-series in finance, quantum states in physics, or embedding spaces in AI, the Blankert Operator offers a unified, customizable framework to detect meaningful patterns—highlighting critical differences when they matter, and ignoring noise when they don’t.

Key Benefits:
⦁ Detects subtle, nonlinear differences missed by standard methods
⦁ Adjustable sensitivity via the α parameter
⦁ Applicable across disciplines: finance, AI, physics, and beyond
⦁ Simple to compute, easy to integrate into modern pipelines