Abstract: Peroxide is a Rust numerical computing library that integrates eight domains in one crate: linear algebra, ordinary differential equation (ODE) solving, adaptive quadrature, spline interpolation with exact polynomial calculus, root-finding, forward-mode automatic differentiation (AD), statistics, and multi-format DataFrame I/O. We designed Peroxide for applied scientists, engineers, and students who need a cohesive numerical environment in Rust without assembling a multi-crate dependency graph.

Abstract: Primordial black holes (PBHs) are significant sources of axions and axion-like particles, provided their Hawking temperature exceeds the particles’ masses. Given the predominant decay of axions into photons, the enhanced photon spectrum they generate can be feasibly detected using sensitive detectors. This paper introduces a novel methodology that elucidates the decay process by which particles traverse and decay over cosmological timescales.

Abstract: Earlier version on arXiv as "Learning Hamiltonian Dynamics with Bayesian Data Assimilation": In this paper, we develop a neural network-based approach for time-series prediction in unknown Hamiltonian dynamical systems. Our approach leverages a surrogate model and learns the system dynamics using generalized coordinates (positions) and their conjugate momenta while preserving a constant Hamiltonian. Additionally, we employ Bayesian data assimilation to refine predictions in real-time using online measurement data.

Comprehensive numerical computing library for Rust, providing functionality comparable to NumPy/SciPy. Core infrastructure for scientific computing research. Published in the Journal of Open Source Software (JOSS), 11(124), 10366.

• Linear algebra with BLAS/LAPACK integration
• Optimization algorithms (Gradient Descent, Levenberg-Marquardt)
• Numerical integration & ODE/PDE solvers
• Statistical distributions & special functions
• DataFrame with multiple I/O formats

Official implementation of operator learning for Hamiltonian mechanics. Explores whether AI can truly understand physical dynamics.

• Four neural architectures (DeepONet, TraONet, VaRONet, MambONet)
• Novel potential generation via Gaussian Random Fields
• Multi-language (Python, Rust, Julia)

Pure Rust implementation of mathematical special functions for scientific computing.

• Gamma, Beta, Error functions
• Regularized and inverse variants
• No external dependencies