Perdigão, Rui A.P. (2025): Information Physical Intelligence in Complex Industrial Systems. https://doi.org/10.46337/uc.250712

This is the official DOI landing page for this work.

The advent of Industry 4.0 has transformed industrial systems through automation, interconnectivity, and data-driven intelligence, along with other emerging technologies empowering the overall industrial paradigm shift (see e.g. Kagermann et al 2013, Lasi et al 2014, Hermann et al. 2016, Rüßmann et al, Lu 2017, Kagermann and Wahlster 2022). Industry 5.0 strengthened the human dimensions in industrial systems, complemented in Industry 6.0 by further technological and human-machine integration advances.

In the meantime, the underlying systems intelligence science has evolved towards a more mathematically robust, physically consistent information science and technology, leveraging the emerging pathways of information physics (Perdigão et al. 2020, Hall and Perdigão 2021), along with the recently developed Augmented Information Physical System Dynamic Intelligence (AIPSI: Perdigão and Hall 2023), Synergistic Nonlinear Quantum Wave Intelligence Networks (SyNQ-WIN: Perdigão and Hall 2024), Information Physical Quantum Technological Intelligence (IPQuTI, Perdigão and Hall 2024), and Neuro-Quantum Cyber-Physical intelligence (NQCPI: Perdigão 2024).

Building from these latest developments with industrial intelligence in mind, we take a leap forward, presenting Information Physical Intelligence (IPI) for Complex Industrial Systems, paving the way towards a next-generation industrial revolution.

The present treatise introduces IPI as a multidisciplinary framework that transcends traditional and state-of-art artificial intelligence and physically informed machine learning by providing an inherently physical system dynamic intelligence. Driven by nature itself rather than traditional digital algorithmic constructs, IPI provides a mathematically robust, physically consistent unified framework empowering next-generation neuro-morphic, causal and cyber-physical process intelligence mechanisms.

With complex industrial systems in mind, IPI further leverages emerging technologies and intelligent materials, empowering industrial systems to achieve unparalleled levels of autonomy, adaptability, and precision. These systems not only process information but also dynamically interact with and manipulate the physical world in real time.

Moreover, by learning from nature IPI enables maximum performance at minimal energy cost. In doing so, our systems operate like our biological nature, providing a far more sustainable and natural alternative to the energy-guzzling infrastructure powering highly demanding applications ranging from quantum computing to artificial intelligence, and more broadly from technological physics to aerospace engineering (see figure below).

Meteoceanics aerospace technologies and equipments leverage the latest capabilities from our Information Physical Intelligence in Complex Industrial Systems (© R.A.P. Perdigão, 2025)

Through our advanced system dynamic modelling, real-time data analytics, adaptive learning and evolutionary cognition, this work equips students, researchers and practitioners with the theoretical and practical expertise to design and manage intelligent industrial systems capable of self-optimization, resilience, and innovation in complex environments. Blending systems engineering, data science, and cyber-physical integration, the treatise prepares the reader to harness post-quantum advancements, nanotechnology, and intelligent materials to drive innovative intelligent solutions for global challenges in manufacturing, energy, logistics, and beyond.

Building from the author’s long years of university teaching, scholarly research and entrepreneurial innovation, this treatise provides solid theoretical, methodological and operational guidance to empower students, lecturers, researchers, practitioners and decision makers. With thorough scientific depth and broad interdisciplinary breadth, this work now accompanies the author’s namesake academic program headquartered at our IUC Physics of Complex Coevolutionary Systems, Institute for Complex System Science.

Related course materials are made available to enrolled participants in our in-house and affiliated academic programs.

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This is the official DOI landing page of:

Title: Information Physical Intelligence in Complex Industrial Systems
Author: Rui A. P. Perdigão
Date: July 12th, 2025
DOI: https://doi.org/10.46337/uc.250712
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REFERENCES

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