REDMOND, Wash., Aug. 28, 2026 (GLOBE NEWSWIRE) -- Pattern Computer®, Inc. (“Pattern” or “the Company”), the global leader in Pattern Discovery, today announced the publication of two foundational physics papers by the Resonance Theory Working Group (“RTWG”). First revealed by Pattern Chair and CEO Mark R. Anderson at the 22nd Annual Future in Review Conference (FiRe 2026), the papers introduce a wave-structure framework for Resonance Theory (“RT”), proposing that the laws of physics derive directly from the properties of space itself and scale consistently from the smallest to the largest dimensions. By advancing a bottom-up understanding of the universe, RT could ultimately transform how goods are manufactured and how humanity interacts with the physical world.
The RTWG operates as a strategic adjunct to Pattern Computer, leveraging the Company’s pattern approach to making deep science discoveries.
RT reframes quantum, statistical, and classical mechanics on the single premise that waves require a medium, characterizing the vacuum as a "Medium of Propagation" (MoP). In this framework, gravity is an emergent refraction effect caused by wave propagation delays around topological defects. This yields a "second-order" gravity theory featuring a higher-order structural term that Einstein anticipated but dismissed as observationally unnecessary at the time. Ultimately, RT unifies quantum mechanics and gravity seamlessly, avoiding the complex assumptions, renormalization, and grid structures required by alternative frameworks like Loop Quantum Gravity.
Foundational Physics Papers
- Introduction to Resonance Theory — Space as a Medium of Propagation: https://doi.org/10.17605/OSF.IO/S6RNV
This paper outlines RT’s core premise that space functions as a medium of propagation, with wave dynamics giving rise to stable matter-like structures and large-scale gravitational behavior.
- Quantum Mechanics as an Interface Grammar of Symplectic Dynamics (Resonance Theory 1): https://doi.org/10.17605/OSF.IO/EJKGF
This paper reframes quantum kinematics as an emergent structural consequence rather than an assumed axiom. It argues that complex Hermitian state space and the Born exponent arise at the boundary where real symplectic wave transport meets a finite, stable receiver, offering a constructive pathway for understanding quantum phenomena through source-receiver geometry.
Mr. Anderson commented, “Science is the foundational engine of human progress, and addressing today's greatest challenges requires a fundamental shift in how we model the universe. At FiRe 2026, we unveiled a unified computational and theoretical framework that resolves the long-standing incompatibility between quantum mechanics and cosmic-scale physics. By modeling space not as an empty vacuum, but as a primary medium that sustains wave structures, RT offers a parsimonious alternative to existing, untestable theories. Mapping these fundamental wave dynamics with our proprietary architecture establishes a powerful new discovery capability—ultimately accelerating high-value commercial breakthroughs in next-generation materials science and scalable quantum computing.”
Anderson added, “RT proposes that resonant fluctuations within the fabric of space give rise to mass and enable the transmission of energy throughout the universe. By connecting phenomena from sub-quantum scales to the cosmos, RT offers a new framework for understanding the physical world as an interconnected system governed by consistent natural laws. Rooted in pattern recognition, RT also underscores the Company’s distinctive pattern discovery approach as both novel and practically powerful—advancing science while opening new pathways for applied problem solving. We believe this framework could support a series of transformative breakthroughs with potential applications including:
- Long-distance communications
- New sensor modalities and improved sensitivity across multiple fields
- Defense-related products and capabilities
- Advanced materials science discoveries
- On-demand design of exotic materials with specified properties
- Improvements in drug design and discovery
- Encryption and cybersecurity
- New computing strategies beyond current-generation quantum machines
“At Pattern, our mission is to solve the world's most complex and deeply entrenched problems by utilizing our innovative discoveries and data-driven insights. Because these breakthroughs have such broad, far-reaching applications, they have the potential to function as a new Theory of Everything. Ultimately, we hope that global recognition of this work will bring our Company the renown it will have earned, helping us translate scientific excellence into widespread commercial success.”
About Resonance Theory
First presented by Mark R. Anderson in 1980, Resonance Theory (“RT”) has evolved into a symplectic-geometry framework for modeling small-scale phenomena, including quantum mechanics, and large-scale phenomena, including astrophysics and cosmology, within a single mathematical structure. Unlike approaches that require renormalization schemes or separate mathematical languages across spatiotemporal scales, RT treats waves as the basis for both information transmission and the stability of matter, modeling matter as standing waves supported by long-lived topological defects in an underlying medium. Beginning from the premise that waves require a medium through which to propagate, RT uses a minimal axiomatic framework of eight postulates to derive an effective field theory consistent with quantum mechanics and the standard model of particle physics, with key principles emerging as theorems, lemmas, and corollaries. The same framework also supports astrophysical modeling and offers an alternative to General Relativity plus ΛCDM (Cold Dark Matter) for predicting galactic and cosmic behavior. More broadly, RT provides a common basis for comparing diverse predictive systems that rely on different underlying ontics.
About Pattern
Pattern Computer, Inc. is a next-generation AI platform company that uses its Pattern Discovery Engine™ to solve important and intractable problems in business and medicine. Its proprietary mathematical techniques in advanced AI identify complex patterns in very-high-order data that have eluded detection by much larger systems, including LLMs. As the Company applies its computational platform to drug discovery and diagnostics, it is also making major Pattern Discoveries for partners in sectors including extended biotech, climate challenges and materials science, aerospace manufacturing quality control, veterinary medicine, air traffic operations, equity trading, AI regulatory compliance in the EU, and energy services. See www.patterncomputer.com.
CONTACT: Laura Guerrant-Oiye (808) 960-2642 – laura@patterncomputer.com
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