Scriber Labs - Research Notebook🪶

:favicon: Overview

Scriber Labs is an independent computational research organization exploring how mathematical and computational structure can be discovered, represented, and interpreted across the physical and biological sciences.

Rather than focusing on benchmark performance alone, Scriber Labs emphasizes interpretability, parsimony, and scientific intuition, using small, well-understood systems as teaching and research vehicles. Many projects investigate how prior physical knowledge can be incorporated into machine learning models, while others explore statistical mechanics, nonlinear dynamics, computational neuroscience, and scientific computing as complementary perspectives on structure discovery.

Current research directions include:

• Physics-Informed Machine Learning (PIML): developing interpretable, low-fidelity PINNs for forward and inverse physical problems, with an emphasis on geometric contraints, identifiability, diagnostic analysis, and failure modes.
• Inverse Problems and Operator Learning: studying how latent physical structure, such as Hamiltonians or governing operators, can be inferred from noisy or incomplete observations.
• Statistical Mechanics and Project Phoenix: exploring partition functions, thermodynamic state spaces, and dimensionality reduction using techniques such as Principal Component Analysis (PCA) to better understand emergent macroscopic behavior.
• Nonlinear Oscillator Dynamics: investigating synchronization phenomena through the Kuramoto model, including the development of benchmark datasets for studying coupled oscillator systems.
• Computational Quantum Physics: building educational projects involving Schrödinger operators, quantum Hamiltonians, and self-consistent field methods as foundations for future work in electronic structure and density functional theory.
• Computational Neuroscience: creating interactive educational tools and simulations that connect mathematical models with biological systems.

Although these projects span multiple scientific disciplines, they share a common objective: to understand how meaningful structure emerges from data, mathematics, and physical laws, and to develop computational tools that make those structures easier to discover, interpret, and communicate.