MatHBioS Workshop

Title: Initiation to Neural Networks with PyTorch.

Speaker: Prof. Hamza El Mahjour (Abdelmalek Essaâdi University, MA).

Date: December 9, 2025 | from 10:00 to 12:00 and from 14:30 to 15:30.
          December 10, 2025 | from 10:00 to 12:00.
          December 11, 2025 | from 14:30 to 15:30.

Place: Room 2.23 - Building IX.

Abstract: This training is designed for PhD students with prior Python experience. It is a five-hour workshop that introduces participants to the practical implementation of neural networks using PyTorch, with a strong emphasis on hands-on coding and real-time model development. Designed for those already familiar with Python, the sessions will guide you from foundational concepts to building, training, and evaluating deep learning models. Through interactive exercises, participants will gain confidence in using PyTorch as a flexible and powerful framework for research applications.

*Those who are interested are invited to complete the form at this link: (https://docs.google.com/forms/d/e/1FAIpQLSckWV1aZjUmXXaW8xieqpTDuxkQSl74OqzZM6cmgQvWWeqdkw/viewform?pli=1)

Title: Fractional Derivatives: Applications in data fitting and Artificial Neural Networks.

Speaker: Prof. Hamza El Mahjour (Abdelmalek Essaâdi University, MA).

Date | Time: December 10, 2025 | from 14:00 to 14:30.

Place: Room 2.23 - Building IX.

Abstract: Fractional calculus introduces non-integer-order derivatives with a built-in memory effect, enabling more accurate modeling of history-dependent phenomena than classical integer-order derivatives.This talk focuses on that memory property and its practical impact. A clear example is the work of Barros et al. (2021): a fractional Caputo SIR model for COVID-19 that incorporates transmission hysteresis and reduces fitting errors by 10-20\% on real data (Italy, South Korea) compared to the standard SIR model, using an adapted Adams–Bashforth–Moulton scheme.I will also briefly review the integration of fractional derivatives into artificial neural networks. By employing Caputo-type fractional operators, these networks gain memory-dependent dynamics and additional tunable parameters (the fractional orders), leading to improved accuracy, stability, adaptability, and ability to capture long-range dependencies in tasks from control and modeling to image processing and medicine.Overall, the memory effect of fractional derivatives provides tangible advantages in both epidemiological modeling and next-generation neural networks, making complex systems easier to represent and control.

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Title: An Introduction to Fractional Calculus: Theory and Challenges.

Speaker: Prof. Aadil Lahrouz (Abdelmalek Essaâdi University, MA).

Date | Time: December 10, 2025 | from 14:30 to 15:00.

Place: Room 2.23 - Building IX.

Abstract: This talk gives an introduction to fractional calculus, which extends classical calculus to non-integer orders. We start with the basic concepts, including the definitions of fractional integrals and fractional derivatives. We focus mainly on two approaches: the Riemann-Liouville and Caputo definitions. We explain their main properties and the key differences between these formulations in different function spaces. Next, we present the main theory of fractional differential equations. We cover existence and uniqueness results, continuation and blow-up theorems. We also explain how fractional differential equations differ from classical ones, particularly in their memory effects and non-local behavior. Throughout the presentation, we point out several important theoretical challenges and open questions in the field.

MatHBioS Workshop

Title: Initiation to Neural Networks with PyTorch.

Speaker: Prof. Hamza El Mahjour (Abdelmalek Essaâdi University, MA).

Date: December 9, 2025 | from 10:00 to 12:00 and from 14:30 to 15:30.
          December 10, 2025 | from 10:00 to 12:00.
          December 11, 2025 | from 14:30 to 15:30.

Place: Room 2.23 - Building IX.

Abstract: This training is designed for PhD students with prior Python experience. It is a five-hour workshop that introduces participants to the practical implementation of neural networks using PyTorch, with a strong emphasis on hands-on coding and real-time model development. Designed for those already familiar with Python, the sessions will guide you from foundational concepts to building, training, and evaluating deep learning models. Through interactive exercises, participants will gain confidence in using PyTorch as a flexible and powerful framework for research applications.

*Those who are interested are invited to complete the form at this link: (https://docs.google.com/forms/d/e/1FAIpQLSckWV1aZjUmXXaW8xieqpTDuxkQSl74OqzZM6cmgQvWWeqdkw/viewform?pli=1)

In this talk, we revisit compartmental models, given by systems of ordinary differential equations, that describe the transmission dynamics of specific infectious diseases. These models are then generalized through hybrid frameworks and complex networks, enabling their application to a large number of evolution problems in fields such as sociology, economics, geography and epidemiology. Optimal control methods are applied to these models and complex networks aiming to mitigate epidemic outbreaks. In the second part of the talk, we propose a controlled complex network of Lotka-Volterra systems, where the strength of the migrations of biological populations are represented by control functions, reproducing the implementation of ecological corridors. We establish synchronization-type results, and the solutions to the optimal control problems demonstrate the potential to restore biodiversity in heterogeneous habitats. This is achieved by reaching either a global coexistence equilibrium or, in a more favorable scenario, a global limit cycle-ensuring sustained biological oscillations and vibrant ecological dynamics.