This repository contains code and datasets for estimating turbulent flow using Physics-Informed Neural Networks (PINNs). The project focuses on solving the Navier-Stokes equations for fluid dynamics and modeling velocity fields around periodic hills using PirateNET, a specialized deep learning framework. The study aims to demonstrate the integration of physical laws into neural networks for accurate turbulence modeling.
- Solve the Navier-Stokes equations for fluid flow simulations.
- Use PINNs to model complex phenomena in fluid dynamics.
- Compare the performance of PirateNET against traditional methods like Multi-Layer Perceptrons (MLPs).
- Validate model predictions using key metrics such as L2 loss and L2 accuracy scores.
- Physics-Informed Neural Networks (PINNs): Integrating physical principles (Navier-Stokes) directly into the training process.
- PirateNET: Residual adaptive network used for fluid dynamics modeling.
- Optimization:
- Adam Optimizer: Used for training the model.
- L-BFGS Optimizer: Employed for fine-tuning.
- Accuracy Evaluation:
- L2 Loss: A common metric to measure model accuracy.
- L2 Accuracy Scores: Used to assess model's ability to predict velocity fields.
- The dataset includes simulated fluid dynamics data based on turbulence models: k-ε, k-ω, k-ε-ϕt-f, k-ω SST.
- Focus on the periodic hills configuration for turbulence modeling.
- Computational Fluid Dynamics (CFD): Solving complex flow simulations.
- Physics-Informed Deep Learning: Integrating physical laws into AI models.
- Turbulence Modeling: Predicting and analyzing turbulent flows with deep learning.
- Data Efficiency: Reducing the need for large datasets by incorporating physics.
Physics-Informed Neural Networks (PINNs), PirateNET, Navier-Stokes Equations, Fluid Dynamics, Turbulence Modeling, Velocity Fields, Deep Learning, Artificial Intelligence (AI), Optimization, Adam Optimizer, L-BFGS Optimizer, Residual Networks, L2 Loss, L2 Accuracy Scores, Turbulence Models, k-ε, k-ω SST, Computational Fluid Dynamics (CFD), Data Efficiency, Machine Learning, Physics-Informed Deep Learning, Simulation Data, Turbulent Flow Prediction.