We present a unified conceptual scaffold in which the observable universe is described as if it were the deployed artifact of a learning/optimization process. Three interlocking theses: (I) the apparent randomness of quantum mechanics reframed as a projection artifact — the distortion produced when a low-dimensional observer samples a deterministic process in a much higher-dimensional space, in analogy with the grokking phenomenon in neural networks; (II) the universe's dimensional reduction reframed as a grokking-style generalization phase transition, with the cosmological constant playing the role of residual loss and "missing" extra dimensions pruned rather than compactified; (III) the arrow of time reframed as causal hardening — one dimension losing backward traversal under an irreversible constraint, with causality as the asymmetric regularization required to avoid computational deadlock. This is a compressive metaphor and a position paper, not a claim of new physical results; it includes explicit scope/non-claims, small-scale machine-learning experiments motivating the analogy, testable directions within ML systems, and limitations.
This deposit contains the field data, synthetic training datasets, trained network weights and analysis code supporting the article "Physics-informed neural network inversion of electrical resistivity tomography data: amortized optimization with field validation in the Moroccan M…
Machine-learning surrogates for the effective elastic properties of mechanical metamaterials are almost always trained and evaluated within a single parametric family of unit-cell geometries, with the test set drawn from the same generator as the training set. Under this protocol…
This position paper formally establishes the principles of Structural Copyright and Explainability within Technorhetoric Version 3.0. Generative AI systems increasingly replicate not only textual content but also underlying conceptual and rhetorical structures. These structures c…
Machine Learning (ML) predictions of cement and concrete quality and subsequent data-driven mixture optimization has been advertised for almost three decades. However, supervised ML leverages correlations, not causal relationships. Aiming for hybrid models, we derive the first ca…
Silicon carbide is a key wide-bandgap semiconductor material for next-generation power electronics, yet the Physical Vapor Transport (PVT) method used for bulk crystal growth remains constrained by complex thermal-chemical interactions and low growth rates. This study develops a…
This repository contains the data products and code required to reproduce the results presented in the article "Deep Learning Models for Estimating Volume and Lorey's Height Across Nordic Countries Using Optical and SAR Satellite Images". The study investigates the use of U-Net d…