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● Live Workshop
Physics-Informed Neural Networks (PINNs)
Physics-Guided AI for Faster, Data-Efficient Modeling
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About This Course
Learn how Physics-Informed Neural Networks (PINNs) combine data with physics-based ODE/PDE constraints to build accurate, data-efficient models for simulation, inverse problems, and parameter estimation.
AIM
To enable participants to build Physics-Informed Neural Networks (PINNs) that fuse data with physics constraints to solve ODE/PDE-driven modeling and inverse problems.
Workshop Objectives
- Build a strong foundation in PINNs and scientific machine learning.
- Formulate physics constraints using ODEs/PDEs and boundary/initial conditions.
- Train PINNs for forward modeling (solution prediction) and inverse problems.
- Perform parameter estimation and system identification using limited data.
- Implement PINNs workflows in Python using modern deep learning frameworks.
- Interpret results, debug training issues, and validate physical consistency.
Workshop Structure
Day 1 – Conceptual Understanding + Basic Implementation
- Introduction to Scientific Machine Learning and limitations of data-only models
- What are Physics-Informed Neural Networks (PINNs)? Core idea and architecture
- Governing equations: ODEs, PDEs, boundary & initial conditions
- Loss function formulation: data loss + physics residual loss
- Training workflow and optimization challenges
Hands On:
- Implement a simple neural network in Python (TensorFlow/PyTorch)
- Solve a basic ODE using a PINN framework
- Visualize predicted vs analytical solution
Day – 2 Solving PDEs and Advanced PINN Techniques
- Extending PINNs to PDE problems
- Automatic differentiation for computing derivatives
- Handling boundary and initial conditions effectively
- Training stability, convergence issues, and scaling strategies
- Introduction to inverse problems
Hands On:
- Implement a PINN to solve a simple PDE (e.g., Heat Equation or Burgers’ Equation)
- Apply boundary conditions and compute residual errors
- Analyze training behavior and improve convergence
Day -3 Inverse Problems, Parameter Estimation & Real Applications
- Inverse problems and parameter identification using PINNs
- Data-efficient learning with sparse measurements
- Applications in engineering, fluid mechanics, materials, and physics
- Model validation and physical consistency checks
- Limitations and future directions of PINNs
Hands On:
- Solve an inverse problem (estimate unknown parameter in a differential equation)
- Train a PINN with limited synthetic data
- Evaluate prediction accuracy and interpret results
Who Should Enroll?
- Undergraduate and postgraduate students in Engineering, Physics, Mathematics, or related fields.
- Ph.D. scholars and researchers working on computational modeling or scientific simulations.
- Faculty members and academicians interested in scientific machine learning.
- Industry professionals in AI/ML, data science, or engineering domains.
- Participants with basic knowledge of Python and differential equations.
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