**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.

Person# Simone Deparis

Official source

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 units

Loading

Courses taught by this person

Loading

Related research domains

Loading

Related publications

Loading

People doing similar research

Loading

Related research domains (90)

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

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

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 (76)

Loading

Loading

Loading

People doing similar research (100)

Courses taught by this person (3)

ENG-643: Teaching problem solving

Problem solving is a core engineering skill. This course explores relevant heuristics, epistemologies, metacognitive skills and evidence-informed teaching strategies for developing problem solving skills that transfer from paper-based exercises to complex, real world engineering situations.

MATH-101(pi): Analysis I (flipped classroom)

Étudier les concepts fondamentaux d'analyse et le calcul différentiel et intégral des fonctions réelles d'une variable.
Cette classe est donné sous forme inversée.

MATH-251(c): Numerical analysis

Le cours présente des méthodes numériques pour la résolution de problèmes mathématiques comme des systèmes d'équations linéaires ou non linéaires, approximation de fonctions, intégration et dérivation, équations différentielles.

Related units (8)

Pablo Antolin Sanchez, Annalisa Buffa, Simone Deparis, Felipe Figueredo Rocha

Effective properties of materials with random heterogeneous structures are typically determined by homogenising the mechanical quantity of interest in a window of observation. The entire problem setting encompasses the solution of a local PDE and some averaging formula for the quantity of interest in such domain. There are relatively standard methods in the literature to completely determine the formulation except for two choices: i) the local domain itself and the ii) boundary conditions. Hence, the modelling errors are governed by the quality of these two choices. The choice i) relates to the degree of representativeness of a microscale sample, i.e., it is essentially a statistical characteristic. Naturally, its reliability is higher as the size of the observation window becomes larger and/or the number of samples increases. On the other hand, excepting few special cases there is no automatic guideline to handle ii). Although it is known that the overall effect of boundary condition becomes less important with the size of the microscale domain, the computational cost to simulate such large problem several times might be prohibitive even for relatively small accuracy requirements. Here we introduce a machine learning procedure to select most suitable boundary conditions for multiscale problems, particularly those arising in solid mechanics. We propose the combination Reduced-Order Models and Deep Neural Networks in an offline phase, whilst the online phase consists in the very same homogenisation procedure plus one (cheap) evaluation of the trained model for boundary conditions. Hence, the method allows an implementation with minimal changes in existing codes and the use of relatively small domains without losing accuracy, which reduces the computational cost by several orders of magnitude. A few test cases accounting for random circular and elliptical inclusions are reported aiming at proving the potentials of the DeepBND method.

2023The 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