Publication briefs
Author: Rui A. P. Perdigão
Date: December 2025
Title: Augmented Information Physical Geointelligence (AIPG)
DOI: https://doi.org/10.46337/251208
Indexed: Yes (Crossref)
Cite as:
Perdigão, R.A.P. (2025): Augmented Information Physical Geointelligence (AIPG). https://doi.org/10.46337/251208
Methodological Keywords: Systems Intelligence, Information Physics, Information Theory, Complex Systems, Mathematical Physics, Data Assimilation, Model Design, Quantum Thermodynamics, Quantum Electrodynamics, Quantum Technologies, Quantum Gravitation, Neuro-Quantum Cyber-Physical Intelligence.
Applied Keywords: Aerospace Engineering, Big Data Analytics, Artificial Intelligence, Physical Intelligence, Machine Learning, Natural Hazards, Extreme Events, Quantum Information Technologies, Quantum Satellites, Quantum Constellation, Quantum Sensing, Quantum Simulation, Multi-Hazards.
Summary
State-of-the-art data analytics, modelling and systems intelligence frameworks are grounded on algebraic structures assuming a core set of invariants and symmetries, e.g. traditional laws in a forward modelling context, or data-driven patterns and associated derived formulations in an
inverse modelling context.
Simulations generated by such approaches yield outcomes living within the same structural canvas. For instance, classical operations act upon points on an affine space, or vectors on a vector space, while quantum operations act upon functional objects living in a Hilbert space. All such produce outcomes of the same algebraic category and living in the same space, revolving around combinations of preexisting configurations and sequences, thereby being inherently endogamic in their web of recurrence.
The notion of innovation is thus fundamentally elusive to machine learning, deep learning, artificial intelligence and generative artificial intelligence. They produce what their algebraic structure is designed to encompass, being locked in that preset universe. This poses a fundamental challenge, which provides a crucial opportunity. An opportunity to see beyond the data. To work beyond the data. To model beyond the data. And ultimately to understand beyond the data. That is the key mission of the present work.
We hereby introduce our recently developed Augmented Information Physical GeoIntelligence (AIPG). AIPG provides a mathematically robust, physically consistent uniLed framework leaping beyond the data-informed canvas to shape new possibilities, venturing not only into the unknown, but also the unknown unknown. One that is not only able to simulate predetermined “emergence” but also predicting unprecedented innovation, including elusive black swan system creation, coevolution and collapse.
In practice, AIPG further empowers the prediction of elusive system dynamic innovation such as new typologies of multi-hazards in a changing world where the future can no longer be drawn from the canvas of the past. This brings added value to a diversity of impactful environmental services, such as geophysical prediction systems, early warning and decision support platforms for emergency preparedness and response, along with long-term planning e.g. for agricultural and water resources management.
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Notes
An overview of this work is also presented at the American Geophysical Union (AGU) Annual Meeting Meeting 2025, on December 16th, 2025.
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