infdx

Infdx is a hypothetical open-source software library designed to support modeling, simulation, and inference for dynamical systems described by differential equations. It is intended to serve researchers, educators, and developers by providing a cohesive workflow from model specification to parameter estimation and uncertainty quantification.

Scope and capabilities: It handles ordinary differential equations and, in extended variants, partial differential equations and delay differential equations. It includes a modular model specification interface (Python-based and optionally domain-specific language), automatic differentiation for gradient-based inference, a suite of numerical solvers for stiff and non-stiff systems, and facilities for events, initial and boundary conditions, and data integration from experiments or observations.

Inference and analysis: The toolkit supports both Bayesian and frequentist approaches to parameter estimation, with backend solvers for Markov chain Monte Carlo, variational inference, and optimization-based methods. It provides tools for identifiability analysis, profile likelihoods, and uncertainty propagation, along with experiment tracking to ensure reproducibility.

Architecture and ecosystem: infdx is designed as a modular core with pluggable backends for solvers and inference engines. It emphasizes interoperability with existing scientific computing stacks, including NumPy, SciPy, and visualization tools, and aims to export models in common formats for sharing and reuse.

Status and usage: As a hypothetical project, infdx is used in illustrative examples and teaching materials to demonstrate end-to-end workflows for dynamical systems. In real-world contexts, similar software projects exist under various licenses, and naming conventions may differ.