Domains
Research
Ten interconnected domains forming the foundation of cross-domain research. Each domain contributes unique tools and perspectives to the study of how the same mathematical structures appear across physics, information theory, and machine learning.
Mathematics
Algebra, linear algebra, calculus, probability, combinatorics, and discrete mathematics - the universal language of structure.
Analysis & Spectral Methods
Oscillations, Fourier analysis, wavelets, spectral decomposition, and differential equations - extracting structure from signals.
Physics
Classical mechanics, electrodynamics, thermodynamics, and statistical mechanics - the laws governing physical systems.
Information Theory
Shannon entropy, KL-divergence, coding theory, and the physical nature of information.
Computation & Algorithms
Graph theory, computability, algorithmic complexity, numerical methods, and Monte Carlo simulations.
Quantum & Frontiers
Quantum mechanics, QFT, general relativity, black holes, holographic principle, and information paradox.
Neuroscience & Cognition
Brain rhythms, EEG spectral analysis, neural oscillations, and the bridge between biological and artificial networks.
Machine Learning
From perceptrons to transformers - neural architectures, optimization, loss functions, and representation learning.
Spectral ML
Spectral analysis of neural networks, SVD of weights, physics-informed architectures, and cross-domain spectral methods.
Systems & Hardware
Arduino, sensors, signal acquisition, FFT pipelines, and real-time processing systems.
Methodology
Each research domain maps to a set of articles, experiments, and computational tools. Together they form a unified framework for understanding how structure encodes information across physical systems.