mathisgerdes/continuous-flow-lft
Continuous normalizing flow for lattice quantum field theory
This project helps theoretical physicists and researchers in quantum field theory to model lattice quantum field theories, specifically focusing on scalar field theory like ϕ⁴ theory. It takes in configurations of a quantum field on a lattice and produces new, statistically independent samples that accurately represent the system's quantum state. Researchers studying the properties and dynamics of quantum fields would use this to generate samples more efficiently than traditional methods.
No commits in the last 6 months.
Use this if you need to generate statistically sound samples for lattice quantum field theory simulations, particularly for scalar field theories, and want to explore methods beyond standard Monte Carlo.
Not ideal if your primary focus is on developing general-purpose machine learning models or if you are not working within the domain of lattice quantum field theory.
Stars
13
Forks
4
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Aug 23, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mathisgerdes/continuous-flow-lft"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
lucidrains/rectified-flow-pytorch
Implementation of rectified flow and some of its followup research / improvements in Pytorch
probabilists/zuko
Normalizing flows in PyTorch
davidnabergoj/torchflows
Modern normalizing flows in Python. Simple to use and easily extensible.
keishihara/flow-matching
Flow Matching implemented in PyTorch
LukasRinder/normalizing-flows
Implementation of normalizing flows in TensorFlow 2 including a small tutorial.