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pykitPIV 1.0 documentation

pykitPIV 1.0 documentation

Installation

Installation instruction

Preliminaries

Virtual wind tunnel with pykitPIV
Indexing convention
Randomization of experimental settings
Image generation parameters cheatsheet

Quickstart

Quickstart your image generation
A gallery of examples

User Guide

Class: Particle
Class: FlowField
Class: Motion
Class: Image
Class: Postprocess
Module: ml

Tutorials

Generate synthetic PIV images
Image statistics
Radial flows
Potential flows
Simplified Langevin model (SLM)
Upload an external velocity field
Generate temporal sequence of PIV images
Postprocess PIV images
Feature size estimation tool for PIV images
Create a PyTorch data loader
Integrate synthetic image generation with training a convolutional neural network (CNN)
Create a Gymnasium reinforcement learning environment
Single deep Q-learning
Upload a trained reinforcement learning model
Modeling out-of-plane particles
Modeling astigmatic PIV
Generate synthetic BOS images
Computing sensory cues and rewards for reinforcement learning
Double deep Q-learning with memory replay
Create a TensorFlow or Keras data loader
Train a convolutional variational autoencoder (CVAE)
Synthetic PIV of a Taylor-Green vortex
Single convolutional deep Q-learning

Computing sensory cues and rewards for reinforcement learning¶

This tutorial can be accessed from the Jupyter notebook:

nbviewer
GitHub
Binder

Double deep Q-learning with memory replay

Generate synthetic BOS images

Copyright © 2025, Kamila Zdybał, Claudio Mucignat, Stefan Kunz, Ivan Lunati

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