Datasets:
Formats:
csv
Size:
10K - 100K
Tags:
computational-fluid-dynamics
physics-informed-neural-networks
navier-stokes
machine-learning
fluid-dynamics
License:
| title: Navier-Stokes Simulated Flow Dataset for PINNs | |
| emoji: 🌊 | |
| license: mit | |
| tags: | |
| - computational-fluid-dynamics | |
| - physics-informed-neural-networks | |
| - navier-stokes | |
| - machine-learning | |
| - fluid-dynamics | |
| # Navier-Stokes Simulated Flow Dataset for PINNs | |
| ## Welcome to the Dataset! | |
| Dive into the dynamic world of fluid flow with the **Navier-Stokes Simulated Flow Dataset for PINNs**! This collection of **10,000 simulated data points** captures the essence of fluid dynamics in a 2D channel, tailored specifically for training **Physics-Informed Neural Networks (PINNs)**. With an even split of **5,000 laminar flow** and **5,000 turbulent flow** points, this dataset is perfect for researchers, data scientists, and students exploring how to model fluid behavior using cutting-edge machine learning techniques. Whether you’re studying smooth laminar flows or chaotic turbulent ones, this dataset offers a compact yet representative resource to power your PINN experiments. | |
| ## Context | |
| The **Navier-Stokes equations** are the cornerstone of fluid dynamics, describing how fluids move under forces like pressure and viscosity. Solving these equations is a challenge, especially for turbulent flows, where chaos reigns. Traditional numerical solvers, like direct numerical simulation (DNS), are computationally expensive, but PINNs offer a promising alternative by embedding the equations into neural networks. This dataset, inspired by PINN research (e.g., the paper on PINNs for Navier-Stokes), provides simulated flow data to train models that learn the physics of fluids directly from data and equations. It’s a bridge between computational fluid dynamics and machine learning, ideal for advancing research and education. | |
| ## Dataset Description | |
| ### Content | |
| The dataset contains **10,000 rows** of simulated flow data in a 2D channel, evenly divided between **5,000 laminar flow** and **5,000 turbulent flow** points. Each row represents a point in a **spatial-temporal grid** with the following features: | |
| - **x**, **y**: Spatial coordinates in the 2D channel (in meters). | |
| - **t**: Time coordinate (in seconds). | |
| - **u**, **v**: Velocity components in the x- and y-directions (in m/s, non-zero). | |
| - **p**: Pressure at the point (in Pa). | |
| - **u_x**, **u_y**, **u_t**: Spatial (∂u/∂x, ∂u/∂y) and temporal (∂u/∂t) derivatives of u. | |
| - **v_x**, **v_y**, **v_t**: Spatial (∂v/∂x, ∂v/∂y) and temporal (∂v/∂t) derivatives of v. | |
| - **p_x**, **p_y**, **p_t**: Spatial (∂p/∂x, ∂p/∂y) and temporal (∂p/∂t) derivatives of p. | |
| - **flow_type**: Label indicating `laminar` or `turbulent` flow. | |
| ### Simulation Details | |
| - **Laminar Flow**: Generated using the analytical **Poiseuille flow solution** with added noise to ensure non-zero transverse velocities (v ≠ 0), mimicking realistic detector-like data. | |
| - **Turbulent Flow**: Created by perturbing Poiseuille flow and evolving it with a basic **Navier-Stokes solver**, incorporating random noise to simulate turbulent behavior. | |
| - **Purpose**: Designed to provide a balanced, compact dataset for PINN training, with derivatives included to enforce physics constraints in the loss function. | |
| ### Format | |
| - **File**: Stored as a CSV file (e.g., `navier_stokes_flow.csv`) in the `data/` directory. | |
| - **Size**: 10,000 rows, with columns for coordinates, velocities, pressure, derivatives, and flow type. | |
| ### Source | |
| The dataset is synthetically generated to emulate flow data for PINN training, inspired by methodologies in PINN research for solving the Navier-Stokes equations. It is curated for public use, enabling researchers to explore fluid dynamics modeling without access to expensive CFD simulations. | |
| ## Use Cases | |
| This dataset is a versatile resource for a range of applications: | |
| - **PINN Training**: Train Physics-Informed Neural Networks to solve the Navier-Stokes equations for laminar and turbulent flows. | |
| - **Machine Learning**: Develop models to predict velocity or pressure fields from spatial-temporal coordinates. | |
| - **Data Visualization**: Create plots of flow fields (e.g., velocity streamlines, pressure contours) to study fluid behavior. | |
| - **Research**: Investigate the differences between laminar and turbulent flows using ML or analytical methods. | |
| - **Education**: Use in CFD or machine learning courses to teach PINN concepts and fluid dynamics. | |
| ## Similar Datasets | |
| Explore these related datasets for additional inspiration: | |
| - **CERN Proton Collision Dataset**: Particle collision data for high-energy physics research. [Link](#) | |
| - **Airfoil Self-Noise Dataset**: Acoustic data for aerodynamic studies. [Link](#) | |
| - **CERN Electron Collision Data**: Electron collision events from CERN experiments. [Link](#) | |
| - **Wind Speed Prediction Dataset**: Meteorological data for wind forecasting. [Link](#) | |
| - **Spanish Wine Quality Dataset**: Chemical properties for wine quality classification. [Link](#) | |
| *Note*: Links are placeholders as specific URLs were not provided. Replace with actual links if available. | |
| ## Acknowledgements | |
| We thank the computational fluid dynamics and machine learning communities for advancing PINN research, particularly the authors of the *Physics-Informed Neural Networks for Solving the Navier-Stokes Equation* paper for inspiring this dataset. The synthetic data was generated to support open science and education, drawing on simplified Navier-Stokes simulations. | |
| For more information about PINNs, explore resources like: https://maziarraissi.github.io/PINNs/ | |
| ## License | |
| MIT License (see `LICENSE` file for details). | |
| --- | |
| Have questions or ideas? Open a GitHub issue or join the discussion on Hugging Face. Happy exploring the flow of fluids! | |