Free transmission problems form a class of free boundary problems in which the solution-dependent discontinuity occurs at the level of the operator. Arising naturally in the modelling of heterogeneous diffusion and related processes, these problems present genuine mathematical difficulties, especially in the fully nonlinear setting. I will discuss recent progress on the existence of viscosity solutions and on their optimal regularity, both in the interior and up to the (fixed and free) boundary. I also plan to mention a few results on the development of numerical schemes tailored to this class of problems. Time permitting, I will conclude with a selection of open questions and possible directions for future research.

Speaker: Prof. Marta Gomes, Instituto Superior Técnico, University of Lisbon

Date | Time: December 5, 2025 | 14h30

Place:NOVA FCT, Departamento de Informática , sala232

Title: Spatialmulti-criteria decision analysis for rehabilitation priority ranking: acollaborative application to heritage workforce housing sites ( Co-authors: AnaPaula Falcão, Rita Machete, Alexandre B. Gonçalves )

Abstract: This workpresents a methodology to rank heritage sites regardingrehabilitation,considering both the characteristics of building sites and ofthe urban environment in the surrounding area. The objective is to aid thedecision process of building rehabilitation byranking the sites according totheir potential for re-emergence in the affordable housing rental market. Thedeveloped methodology is based on a combination of multi-criteria decisionanalysis (MCDA) and spatial analysis of geographical data, in order toconstruct an index, the “rehabilitation potential”, which is understandable byrehabilitation technicians and land managers and is applicable to support a listof priorities of building rehabilitation interventions. The methodology wasapplied to a case study consisting of aset of 33 heritage sites of theworkforce housing typology in Lisbon. These were built in the early industrialage in Portugal and are owned by the city municipality. The application ofMCDAwas a collaborative process that brought together the expertise of the academyand of the public administration. The results included a sensitivity analysisand gave form to a recommendation of five sites, selected from the totalworkforce housing set, to be rehabilitated in the near future.

Short bio: Prof. Marta Gomes is anAssistant Professor at Instituto Superior Técnico (IST), Department of CivilEngineering, Architecture and Environment, and a researcher at CERIS. She holdsa PhD in Systems Engineering (2007) from IST and has extensive expertise inoperations research, optimization, discrete-event simulation, multi-criteriadecision analysis, and large-scale data analytics, with applications acrosscivil engineering, industrial engineering, mechanical and aerospace systems.Her career includes a strong record of publications and collaborative research projects inmaintenance optimization, transport systems, pavement engineering, and urbanand environmental planning. 

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)

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.

#####################################################

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)