Workshop

AI & Physics-Informed
Neural Networks

Bridging Artificial Intelligence and Mathematical Modeling through Deep Learning & PINNs.

About the Workshop

Step into the future of computational science

As part of the First International Conference on Advances in Operator Theory and Applications (ICAOTA'2026), this immersive workshop offers a cutting-edge journey into the fusion of AI and differential equations — from theoretical foundations through real-world applications.

Workshop Details
  • Location
    Hammamet, Tunisia
  • Dates
    March 24–25–26, 2026
  • Duration
    5 hours (2 sessions)
  • Instructor
    Pr. Bassem Ben Hamed
  • Registration
    Free for ICAOTA'2026 registrants
Participants receive a signed Certificate of Participation upon completion.
Workshop Agenda
Session 1 — Foundations & First Implementation (2.5h)
  • Theory of PINNs & Universal Approximation Theorem
  • PINN architecture & loss function design
  • Lab: Solve a second-order ODE — Harmonic Oscillator
Session 2 — PDEs & Real-World Applications (2.5h)
  • PINNs for PDEs and inverse problems
  • Boundary/initial condition enforcement strategies
  • Lab: Solve the 1D Diffusion Equation using PyTorch/TensorFlow

Learning Outcomes

  • Master PINN theory and architecture
  • Build models with physical constraints
  • Gain TensorFlow/PyTorch experience
  • Bridge AI and applied mathematics

Tools & Technologies

  • Python 3.x
  • TensorFlow or PyTorch
  • Jupyter Notebooks
  • NumPy / SciPy

Who Should Attend?

  • Graduate students & postdocs
  • Professors & scientific programmers
  • Engineers & data scientists in modeling

Prerequisites

Basic linear algebra, differential equations, and Python familiarity.