{"id":1350,"date":"2025-12-13T15:43:01","date_gmt":"2025-12-13T15:43:01","guid":{"rendered":"https:\/\/meteoceanics.org\/icss\/?page_id=1350"},"modified":"2025-12-13T15:53:52","modified_gmt":"2025-12-13T15:53:52","slug":"generalized-categorical-physical-intelligence-gcpi-deciphering-non-ergodic-nonlinear-nonlocal-multiscale-spatiotemporal-neo-systemic-complexity-and-predictability","status":"publish","type":"page","link":"https:\/\/meteoceanics.org\/icss\/publications\/generalized-categorical-physical-intelligence-gcpi-deciphering-non-ergodic-nonlinear-nonlocal-multiscale-spatiotemporal-neo-systemic-complexity-and-predictability\/","title":{"rendered":"Generalized Categorical Physical Intelligence (GCPI): Deciphering Non-Ergodic Nonlinear Nonlocal Multiscale Spatiotemporal Neo-Systemic Complexity and Predictability"},"content":{"rendered":"

Publication briefs<\/strong><\/p>\r\n

Author: Rui A. P. Perdig\u00e3o
Date: December 2025
Title: Generalized Categorical Physical Intelligence (GCPI): Deciphering Non-Ergodic Nonlinear Nonlocal Multiscale Spatiotemporal Neo-Systemic Complexity and Predictability.
DOI: https:\/\/doi.org\/10.46337\/251213
Indexed: Yes (Crossref)<\/p>\r\n

Cite as:
Perdig\u00e3o, R.A.P. (2025): Generalized Categorical Physical Intelligence (GCPI): Deciphering Non-Ergodic Nonlinear Nonlocal Multiscale Spatiotemporal Neo-Systemic Complexity and Predictability. https:\/\/doi.org\/10.46337\/251213<\/p>\r\n

Methodological Keywords: Categorical Algebra, Exterior Algebras, Higher-Order Algebraic Structures, Systems Intelligence, Mathematical Physics, Information Physics, Information Theory, Complex Systems, Data Assimilation, Model Design.<\/p>\r\n

Applied Keywords: Scaling, Multifractals, 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.<\/p>\r\n

Summary<\/strong><\/p>\r\n

A novel information physical system dynamic intelligence methodological suite is hereby introduced for retrieving, analyzing, processing and predicting the emergence of generalized spatiotemporal non-ergodic nonlocal nonlinear multiscaling mechanisms in complex system dynamics.<\/p>\r\n

Our new Generalized Categorical Physical Intelligence (GCPI) is constructed enabling the formalization of broader classes of structural-functional interactions and symmetries, along with the prediction of non-recurring critical transitions, symmetry breaks and neo-systemic emergence elusive to prior data records or state-of-art formalisms, shedding light onto system dynamics and predictability even in the absence of the traditional anchoring foundational spaces and symmetries.<\/p>\r\n

Traditional vector, functional or operator space requirements are lifted, as are exponential scaling mechanisms and classical symmetries, thereby allowing for a broader yet still operable construct that does not need to be commutative, distributive and not even associative.<\/p>\r\n

Yet new classes of physically interpretable morphisms and symmetries are unveiled from our GCPI that allow for a full-fledged physically consistent generalized categorical algebra to be systematically derived and operated upon, and new features of system dynamic complexity to be formally unveiled, deciphered and treated.<\/p>\r\n

Practical examples are explored including the generalization of nonlocal spatiotemporal systems exhibiting long-range dynamic entanglement, the unveiling of new classes of multifractal scaling mechanisms within spatiotemporal processes deemed non-fractal under classical algebras yet endowed with novel categorical multifractality in our broader construct, and the extending of post-critical predictability ranges beyond classical system dynamic information collapse inherent to a full structural-functional systemic overhaul.<\/p>\r\n

Operational impacts are also demonstrated and discussed, including in further empowering multi-hazard risk dynamic assessments, entangled multi-extreme event prediction, early warning, prediction and decision support systems, helping further strengthen civil protection mechanisms and long-term planning in the wake of multiscale multidomain systemic disruptions across our environment and society.<\/p>\r\n

Full Document<\/strong><\/p>\r\n

Restricted Access: Login here<\/a><\/p>\r\n

Notes<\/strong><\/p>\r\n

An overview of this work is also presented at the American Geophysical Union (AGU) Annual Meeting Meeting 2025, on December 16th, 2025.<\/p>\r\n

This DOI landing page is currently being updated. Stay tuned for more information. Thank you.<\/em><\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","protected":false},"excerpt":{"rendered":"

Publication briefs Author: Rui A. P. Perdig\u00e3oDate: December 2025Title: Generalized Categorical Physical Intelligence (GCPI): Deciphering Non-Ergodic Nonlinear Nonlocal Multiscale Spatiotemporal Neo-Systemic Complexity and Predictability.DOI: https:\/\/doi.org\/10.46337\/251213Indexed: Yes (Crossref) Cite as:Perdig\u00e3o, R.A.P. (2025): Generalized Categorical Physical Intelligence (GCPI): Deciphering Non-Ergodic Nonlinear Nonlocal Multiscale Spatiotemporal Neo-Systemic Complexity and Predictability. https:\/\/doi.org\/10.46337\/251213 Methodological Keywords: Categorical Algebra, Exterior Algebras, Higher-Order Algebraic … <\/p>\n

Continue reading “Generalized Categorical Physical Intelligence (GCPI): Deciphering Non-Ergodic Nonlinear Nonlocal Multiscale Spatiotemporal Neo-Systemic Complexity and Predictability”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":311,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1350","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/pages\/1350","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/comments?post=1350"}],"version-history":[{"count":7,"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/pages\/1350\/revisions"}],"predecessor-version":[{"id":1357,"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/pages\/1350\/revisions\/1357"}],"up":[{"embeddable":true,"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/pages\/311"}],"wp:attachment":[{"href":"https:\/\/meteoceanics.org\/icss\/wp-json\/wp\/v2\/media?parent=1350"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}