**Are you an EPFL student looking for a semester project?**

Work with us on data science and visualisation projects, and deploy your project as an app on top of GraphSearch.

Unit# The SCI SB SD

Group

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related people

Loading

Units doing similar research

Loading

Related research domains

Loading

Related publications

Loading

Related people (7)

Related research domains (89)

Numerical analysis

Numerical analysis is the study of algorithms that use numerical approximation (as opposed to symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathema

Fluid–structure interaction

Fluid–structure interaction (FSI) is the interaction of some movable or deformable structure with an internal or surrounding fluid flow. Fluid–structure interactions can be stable or oscillatory. In o

Finite element method

The finite element method (FEM) is a popular method for numerically solving differential equations arising in engineering and mathematical modeling. Typical problem areas of interest include the tr

Related publications (89)

Loading

Loading

Loading

Units doing similar research (101)

The Internodes method is a general purpose method to deal with non-conforming discretizations of partial differential equations on 2D and 3D regions partitioned into disjoint subdomains. In this paper we are interested in measuring how much the Internodes method is conservative across the interface. If hp-fem discretizations are employed, we prove that both the total force and total work generated by the numerical solution at the interface of the decomposition vanish in an optimal way when the mesh size tends to zero, i.e., like 𝒪(ℎ𝑝), where p is the local polynomial degree and h the mesh-size. This is the same as the error decay in the H1-broken norm. We observe that the conservation properties of a method are intrinsic to the method itself because they depend on the way the interface conditions are enforced rather then on the problem we are called to approximate. For this reason, in this paper, we focus on second-order elliptic PDEs, although we use the terminology (of forces and works) proper of linear elasticity. Two and three dimensional numerical experiments corroborate the theoretical findings, also by comparing Internodes with Mortar and WACA methods.

2022Simone Deparis, Cécile Hardebolle, Roland John Tormey, Himanshu Verma

Background Research shows that active pedagogies could play an important role in achieving more equitable outcomes for diverse groups of students in Science, Technology, Engineering, and Mathematics (STEM). Although flipped classes are a popular active methodology, there is a lack of high-quality studies assessing their impact in ecologically valid settings and exploring how outcomes are related to gender and to prior education. Purpose This paper presents two modified replications of an experimental study investigating the impact of the flipped class approach on students' achievement in a large, first-year class in an engineering bachelor's degree. Methodology We added a new strand, progressively flipped over 3 years, to eight parallel strands of a high-stakes mandatory linear algebra course for engineers. The study followed a replicated-between-subjects design, with students in the flipped strand learning the same material as those in the other strands and taking the same final exam. Results Our results demonstrate that the flipped format did not have any significant impact on students' achievement compared to traditional lecturing. However, both replications in the flipped condition show a reduced attainment gap for women and students with less prior knowledge in mathematics. Conclusion While the flipped class seems to have weaker effects on learning than other active methodologies, the evidence in this study indicates that it may have an impact on reducing the attainment gap between different groups of students. It may therefore be particularly interesting to consider in efforts to achieve more equitable outcomes for women and where students have heterogeneous educational backgrounds.

2022, , ,

In this work, we present a PDE-aware deep learning model for the numerical solution to the inverse problem of electrocardiography. The model both leverages data availability and exploits the knowledge of a physically based mathematical model, expressed by means of partial differential equations (PDEs), to carry out the task at hand. The goal is to estimate the epicardial potential field from measurements of the electric potential at a discrete set of points on the body surface. The employment of deep learning techniques in this context is made difficult by the low amount of clinical data at disposal, as measuring cardiac potentials requires invasive procedures. Suitably exploiting the underlying physically based mathematical model allowed circumventing the data availability issue and led to the development of fast-training and low-complexity models. Physical awareness has been pursued by means of two elements: the projection of the epicardial potential onto a space-time reduced subspace, spanned by the numerical solutions of the governing PDEs, and the inclusion of a tensorial reduced basis solver of the forward problem in the network architecture. Numerical tests have been conducted only on synthetic data, obtained via a full order model approximation of the problem at hand, and two variants of the model have been addressed. Both proved to be accurate, up to an average $\ell^1$-norm relative error on epicardial activation maps of 3.5%, and both could be trained in \approx$$15 min. Nevertheless, some improvements, mostly concerning data generation, are necessary in order to bridge the gap with clinical applications.

2022