Complexity Science, Information Theory & Computing
"To exist is to continually overcome loss"
Nathan M. Thornhill is an independent researcher working at the intersection of complexity science, information theory, and computational physics. His recent work includes the first quantitative measurement of information loss at dimensional boundaries, discovering a robust 86% scaling law.
Thornhill's research spans two main areas: dimensional information dynamics and the Existence Threshold framework—a physicalist approach to understanding consciousness through information thermodynamics. He's particularly interested in how systems maintain coherence against entropy, whether that's patterns persisting across dimensions, neural networks staying organized, or AI systems preserving information through computational layers.
When not doing research, Thornhill runs a technology consultancy in Fort Wayne, Indiana, and enjoys playing guitar, gardening, and spending time with his wife and daughter.
All publications are archived across multiple platforms to ensure permanent accessibility and proper attribution:
A physicalist framework for understanding consciousness through information thermodynamics. Establishes the theoretical conditions that define the boundary between existence and non-existence, exploring how systems maintain coherence against entropy.
The first quantitative measurement of information loss at dimensional boundaries. Reveals a consistent 86% information retention pattern across cellular automata systems, establishing a universal scaling law for dimensional transitions.
A formal mathematical theorem describing the mechanisms underlying information loss during dimensional reduction. Provides the theoretical foundation for understanding how patterns persist—or fail to persist—across dimensional boundaries.
Systems and Methods for Adversarial Geometric Encoding to Preserve Information Across Dimensional Boundaries
Systems and Methods for Optimal Dimensional Encoding in Neural Networks
Complete Three-Dimensional Geometric Encoding System for Data Preservation and Analysis
For research inquiries, collaboration opportunities, media requests, or general questions:
research@nathanthornhill.com