We believe in open science. Our tools are available on GitHub, and the group actively contributes to and maintains the following libraries:

PiMPC.jl

Parallel-in-horizon, construction-free model predictive control with GPU acceleration

Julia / Python

License: MIT

Method: ADMM-based MPC

MI-DPC-UPHES

Mixed-integer differentiable predictive control for underground pumped-hydro scheduling

Python

License: MIT

Companion code: CDC 2026

CINOC

Cardinality-invariant neural operator policies for scalable multi-agent PDE control

Python / JAX

Framework: Tesseract-JAX

Companion code: ICML 2026

SCORE

Statistical certification of regions of attraction using extreme value theory

Python

Includes: Research code and tutorials

Companion code: CDC 2026

NeuroMANCER

PyTorch library for parametric constrained optimization and control

Python

License: BSD 3-Clause License

Framework: PyTorch