Intérêt (finance) | EPFL Graph Search

En finance, l'intérêt est la rémunération d'un prêt, sous forme généralement d'un versement périodique de l'emprunteur au prêteur. Pour le prêteur, c'est le prix de sa renonciation temporaire à la liquidité. Pour l'emprunteur, c'est un coût correspondant à une utilisation anticipée. Une épargne rémunérée par un intérêt est assimilable à un prêt fait à un emprunteur, comme une banque ou l'organisme bénéficiaire de cette épargne. Calcul de l'intérêt Taux d'intérêt L'intérêt est proportionnel au capital et croît avec le temps couru. Il peut être calculé par application d'un pourcentage annuel, appelé le taux d'intérêt annuel. Ce taux peut être fixe ou ajusté d'une période à l'autre selon une formule d'indexation. Dans ce cas, il peut être révisable (fixé en début de chaque période) ou variable (déterminé à la fin de chaque période). Il varie en fonction de l'inflation et du risque sous-jacent au capital financé, c'est la prime de risque. Calcul de l'intérêt simple

District scale prediction of subsurface waste heat flows

Due to increasing urbanization and scarcity of land, the need to include the underground in urban planning is growing, especially in cities. The human-made underground structures generate an anthropogenic heat flow which has a significant impact on the ground temperature and leads to the creation of urban underground heat islands (UUHI). In parallel with the urbanization, the consumption of energy increases and the interest in sustainable energy production from thermal energy sources is growing. The UUHI have a high geothermal potential and are therefore of great interest for energy production. A sustainable evaluation of the geothermal potential in cities depends however on many factors and is therefore a complex procedure in which different scenarios must be assessed. This master thesis proposes a machine learning (ML) based approach for the evaluation of the underground waste heat flow of buildings. ML techniques are suitable to very quick and accurate modeling and can therefore be of great interest in the field of complex geothermal potential evaluation. In this project we will focus on the ground temperature change due to heat losses of basements. To this effect, 2D heat maps are created for a given depth and a given simulation time period. In this study, a comprehensive validation of the proposed ML model (random forest algorithm) is presented for different small scale scenarios, using various heat source geometries, locations, numbers and thermal loads. Furthermore, we study the heat maps for diverse depths and simulation time periods. To do so, we assume only a conductive heat flow mode and constant boundary conditions. The proposed methodology involves having access to fully solved numerical simulation to create a data set which is used to train and test the ML algorithm. For this purpose, the finite element software package COMSOL is used. We evaluate and discuss the accuracy of the proposed ML approach on each studied scenario. The results promise a big potential all while requiring only few solved FE models. We obtain a root mean squared error of always less than 5 to 10% depending on the scenario. At the end of this paper a district scale application of the proposed ML technique is presented on the Loop district in Downtown Chicago, US. It will be shown that 25% of the total Loop data from the FE model is sufficient to learn the ML model and to predict the whole district with a root mean squared error of less than 5%.

2020

The Scikit HEP Project - overview and prospects

Pratyush Das

Scikit-HEP is a community-driven and community-oriented project with the goal of providing an ecosystem for particle physics data analysis in Python. Scikit-HEP is a toolset of approximately twenty packages and a few "a ffiliated" packages. It expands the typical Python data analysis tools for particle physicists. Each package focuses on a particular topic, and interacts with other packages in the toolset, where appropriate. Most of the packages are easy to install in many environments; much work has been done this year to provide binary "wheels" on PyPI and conda-forge packages. The Scikit-HEP project has been gaining interest and momentum, by building a user and developer community engaging collaboration across experiments. Some of the packages are being used by other communities, including the astroparticle physics community. An overview of the overall project and toolset will be presented, as well as a vision for development and sustainability.

E D P SCIENCES2020

Certified Reduced Basis Method for Parametrized Partial Differential Equations: a Combination with ANOVA Expansion

Denis Devaud

In many fields, the computation of an output depending on a field variable is of great interest. If the field variable depends on a high-dimensional parameter, the computational cost involved can be huge. Hence, it is necessary to find efficient and reliable methods to solve such a problem. In this report, we describe a method to solve in an efficient and reliable way elliptic coercive parametric partial differential equations which depend on high-dimensional parameters. The idea is to combine two methods already known, the Reduced Basis (RB) method and the ANOVA expansion. Since also the use of the Reduced Basis method for the approximation of high-dimensional parametric partial differential equations can be computationally expensive, it is important to find a method to approximate the solution in an efficient way. The method is divided in three steps, RB-ANOVA-RB. First, we use the Reduced Basis method with a big tolerance to have a coarse approximation of the output of interest. This allows us to use the ANOVA expansion in order to determine if there exists some less important parameters. If it is the case, we freeze them and then reapply the Reduced Basis method with a low tolerance to get a fine approximation of the output.

2012

District scale prediction of subsurface waste heat flows

2020