RCIPL
Repositório Institucional do Politécnico de Lisboa
Entradas recentes
Solving steady-state elliptic problems in irregular domains using physics-informed neural networks and fictitious domain methods
Publication . Rodrigues, José; Rodrigues, José Alberto
This paper introduces an innovative methodology for solving steady-state elliptic partial differential equations defined over irregular domains, by coupling the capabilities of Physics-Informed Neural Networks with the Fictitious Domain Method. The primary emphasis is placed on applications involving the heat equation, a fundamental model in thermal analysis where the complexity of non-standard geometries often poses significant challenges for traditional numerical methods. The proposed approach exploits the inherent strength of Physics-Informed Neural Networks in embedding the underlying physical laws directly into the learning process, enabling the model to approximate solutions without relying on meshbased discretization. Simultaneously, the Fictitious Domain Method facilitates the treatment of irregular computational domains by embedding them within a larger, regular domain, thereby simplifying the application of boundary conditions and numerical operations. The synergy between these two techniques results in a flexible, efficient, and accurate computational framework that is well-suited for addressing heat transfer problems in complex geometrical configurations.
Experimental study on drag coefficient of flexible vegetation under non-breaking waves
Publication . Reis, A. Rui; Fortes, Conceição J. E. M.; ; Rodrigues, José Alberto; Hu, Zhan; Suzuki, Tomohiro
Laboratory experiments of wave propagation over rigid and flexible vegetation fields, with the same configurations, were conducted to understand the effect of vegetation flexibility on the drag coefficient (CD). The direct method and the least squares method (LSM), based on force and flow measurements, are applied to calculate the CD in the xperimental conditions. The formulations of both methods are extended to estimate the CD for flexible vegetation cases. A video analysis was performed to account for the swaying motion. Typically, wave dissipation is lower for flexible than for rigid vegetation of the same configuration, under the same flow condition. Therefore, a proportional effect in the corresponding CD results, obtained from common CD calibration to wave dissipation without considering vegetation motion, is usually observed. However, the present results show that although the wave dissipation was 34% lower for flexible relative to rigid vegetation, the respective CD values were close. CD estimations considering vegetation motion and inertia suggest that CD of flexible vegetation was up to 13% higher relative to rigid vegetation. Accounting for inertia reduced the CD for rigid vegetation up to 7%, while raised the CD for flexible vegetation up to 13%.
Using physics-informed neural networks (PINNs) for tumor cell growth modeling
Publication . Rodrigues, José
This paper presents a comprehensive investigation into the applicability and performance of two prominent growth models, namely, the Verhulst model and the Montroll model, in the context of modeling tumor cell growth dynamics. Leveraging the power of Physics-Informed Neural Networks (PINNs), we aim to assess and compare the predictive capabilities of these models against experimental data obtained from the growth patterns of tumor cells. We employed a dataset comprising detailed measurements of tumor cell growth to train and evaluate the Verhulst and Montroll models. By integrating PINNs, we not only account for experimental noise but also embed physical insights into the learning process, enabling the models to capture the underlying mechanisms governing tumor cell growth. Our findings reveal the strengths and limitations of each growth model in accurately representing tumor cell proliferation dynamics. Furthermore, the study sheds light on the impact of incorporating physics-informed constraints on the model predictions. The insights gained from this comparative analysis contribute to advancing our understanding of growth models and their applications in predicting complex biological phenomena, particularly in the realm of tumor cell proliferation.
O papel da formação contínua e da inovação no desempenho organizacional do ensino superior português
Publication . Ribeiro Mucharreira, Pedro; Godinho Antunes, Marina; Texeira Fernandes Justino, Maria do Rosário; Texeira Quirós, Joaquín
Este estudo tem como objetivo avaliar o papel da formação contínua e das estratégias de inovação no desempenho organizacional das instituições de ensino superior (IES), em Portugal. No contexto atual da globalização, as IES devem adotar estratégias que possam promover a qualidade no sentido de aumentar a sua competitividade, garantindo a sua sustentabilidade a médio e a longo-prazo. Em termos metodológicos, a presente investigação recorreu aos dados obtidos num inquérito por questionário dirigido a membros de IES portuguesas. No tratamento dos dados recorreu-se a uma regressão linear múltipla para examinar as relações entre as diferentes variáveis tidas em consideração. Os resultados apontam no sentido de que a formação contínua docente, a inovação, bem como as estratégias de inovação das IES influenciam significativamente o desempenho organizacional destas instituições no contexto português. A investigação reveste-se de particular importância tendo em conta o papel estratégico das IES para o crescimento e desenvolvimento económico de um país.
A finite element method for shear stress study on cancer cell proliferation
Publication . Rodrigues, José; Rodrigues, José Alberto
Metastatic progression of tumors requires the coordinated dissemination of cancerous cells through interstitial tissues and their replication in distant body locations. Despite their importance in cancer treatment decisions, key factors, such as cell shape adaptation and the role it plays in dense tissue invasion by cancerous cells, are not well understood. With this work, we will continue further work on the study of the effect of shear stress on cancer cell proliferation. We present a coupled Keller-Segel and Darcy–Brinkman model implemented in finite elements to obtain numerical results.