UFL - Unified Form Language

Next generation FEniCS Form Compiler for finite element forms

Machine learning algorithms for many-body quantum systems

variPEPS -- Versatile tensor network library for variational ground state simulations in two spatial dimensions

👀🛡️ Code for the paper “Blending adversarial training and representation-conditional purification via aggregation improves adversarial robustness” by Emanuele Ballarin, Alessio Ansuini and Luca Bortolussi (2025)

This is a 'hands-on' tutorial for the RIKEN International School on Data Assimilation (RISDA2018).

Python scripts for trying various data assimilation algorithms with simple toy models

Implementation of the Variational Method for Quantum Mechanics in python.

Projected time-dependent Variational Monte Carlo (p-tVMC) method based on infidelity optimization for variational simulation of quantum dynamics.

Official PyTorch code for UAI 2024 paper "ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-variable Context Encoding"

FlowBasis: Variational solutions of perturbed quantum harmonic oscillator problems via augmented basis sets.

A Python implementation of Monge optimal transportation

Weather routing via variational methods

A GUI for the Variational_Principle repository in PyQT

Variationally enhanced sampling for single-particle langevin dynamics with neural network bias potentials and path collective variables. Based on OpenMM + PyTorch.

Implementation of neural coherent states, arXiv:2105.15193

Gutzwiller variational approach for the Bose-Hubbard model, with simulated-annealing optimization

This is a complete python package that explores variational methods for 2D image segmentation popularly known as snakes. The package consists of already implemented methods like Chan Vese & Yezzi (mean seperation), Bhattacharya (Probability distribution separation), also, Interactive feedback control approach to snakes

Code for A Weighted Mini-Bucket Bound for Solving Influence Diagrams (UAI 2019) and Join-Graph Decomposition Bounds for Influence Diagrams (UAI 2018).