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pykitPIV 1.0 documentation
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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
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Single deep Q-learning¶

This tutorial can be accessed from the Jupyter notebook:

  • nbviewer

  • GitHub

  • Binder

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Upload a trained reinforcement learning model
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Create a Gymnasium reinforcement learning environment
Copyright © 2025, Kamila Zdybał, Claudio Mucignat, Stefan Kunz, Ivan Lunati
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