Data-driven Reverse Engineering of Physical Laws: Extracting Unknown Interactions via Hierarchical Basis Expansion

This record contains a private research note summarizing the Operator Basis Architecture (OBA), a data‑driven inverse‑problem framework for extracting unknown physical interactions from stationary distributions. The content is shared on Zenodo for archival and version‑tracking purposes only and is not publicly accessible.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-07-23
DOI
https://doi.org/10.5281/zenodo.21506067
Primary Topic
Model Reduction and Neural Networks
Type
preprint
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preprint

Data-driven Reverse Engineering of Physical Laws: Extracting Unknown Interactions via Hierarchical Basis Expansion

ab_ab
Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
preprint

Data-driven Reverse Engineering of Physical Laws: Extracting Unknown Interactions via Hierarchical Basis Expansion

ab_ab
preprint en

Abstract

This record contains a private research note summarizing the Operator Basis Architecture (OBA), a data‑driven inverse‑problem framework for extracting unknown physical interactions from stationary distributions. The content is shared on Zenodo for archival and version‑tracking purposes only and is not publicly accessible.

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
DOIOpen Access PDFOpenAlex
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Data-driven Reverse Engineering of Physical Laws: Extracting Unknown Interactions via Hierarchical Basis Expansion — ab_ab · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS