Open-source scientific software projects.
I develop research software following practices used in the software industry: version control, automated testing, code quality checks and documentation. The aim is to make computational methods easier to install, verify, maintain and reuse.
My previous experience in the software industry, developing scientific applications for oil and gas, contributes to this approach. My professional background is described in the CV.
Testing and quality — Automated test suites and checks in continuous integration, with validation cases appropriate to each package’s scope.
Package distribution — Installable Python packages with versioned releases and documented dependencies. Package links are provided with each project below.
Reproducible environments — Explicit dependencies, Pixi environments, examples and execution instructions to help others reproduce the computational work.
Digital porous media
Digital porous media modeling in Python: pore networks, petrophysical properties and single-phase transport simulation.
Its main focus is pore network modeling, with complementary finite-volume, finite-element and lattice Boltzmann methods. The documentation includes examples and distinguishes numerical verification from experimental validation.
Differentiable thermodynamics
Thermodynamic properties and phase equilibria with PyTorch, connecting automatic differentiation, optimization and machine learning.
Includes equations of state, activity coefficient models, and stability and flash calculations. Documented examples cover property evaluation, derivatives and trainable parameters.
Thermophysical data
Access, search and parse experimental ThermoML data in Python, preserving conditions, uncertainties and references for each measurement.
Helps organize published data for analysis and model fitting, with tabular export and source traceability. Examples and notebooks document searching, parsing and handling the results.
Other projects: porousdrake and perphil.