Helios

Helios is a private research archive covering computational astrophysics, Turing challenge exploration, and Bayesian inference with probabilistic programming. The repository serves as a centralized collection of research documentation, literature reviews, and project planning materials across these three research threads. It is proprietary and not open source.

The computational astrophysics component contains materials related to numerical simulation and data analysis techniques applied to astrophysical systems. The Turing challenge exploration thread documents investigations into fundamental questions at the intersection of computation theory and physical systems. The Bayesian inference and probabilistic programming materials cover statistical modeling approaches using probabilistic frameworks for scientific data analysis and uncertainty quantification.

As a research archive rather than a software tool, helios is organized around documentation and planning rather than executable code. Literature reviews compile and annotate relevant publications, while project planning materials outline research directions and milestones for each of the three focus areas.

Helios is linked to the scicomp cross-platform scientific computing framework, which provides computational tools in Python, MATLAB, and Mathematica for quantum mechanics, thermal transport, and physics-informed machine learning. The connection reflects shared interest in computational methods for physical sciences, with scicomp supplying reusable solver infrastructure that can support the numerical work documented in helios. The repository is hosted at github.com/alawein/helios and is under active development with P3 priority in the tools domain.