About the Inventor
I am passionate about the intersection of artificial intelligence and mathematics. I enjoy creating novel mathematical operators that solve real problems—especially in areas like generalization, similarity, robustness, and interpretability. My approach is to develop operators that are both theoretically grounded and practically useful, and to share them with the wider research and applied communities.
If you’re interested in a tailor-made mathematical operator or collaboration, feel free to contact me at blankertjp@gmal.com.
Several of my books on this topic are currently in press, including Mathematics in AI.
Below is a list of my original mathematical inventions, pre-published and awaiting peer review in formal academic venues.
📌 Blankert Operator
The Blankert Operator is a nonlinear generalization of the inner product, designed to measure similarity between functions with tunable sensitivity. By introducing a parameter α\alpha, it allows the user to amplify or suppress functional discrepancies. This operator can behave like a standard inner product when α=0\alpha = 0, or become highly sensitive to small differences when α>0\alpha > 0, making it suitable for contexts where subtle deviations are critical—such as in financial time series, quantum state comparisons, or AI model evaluation. It is symmetric, interpretable, and computationally efficient.
- Full_article_Blankert_Operator_incl_DOI PREVIEW
-
Applications: signal similarity, nonlinear correlation, interpretable AI