Recent Submissions 

  1. ACSICON ADN model data 

    Fuchs, Alexander; Larsson, Mats; Demiray, Turhan (2022)
    The dataset contains the aggregated distribution network (ADN) models developed in the research project "ACSICON" and published in the paper [1]. The zip files contains data for different penetration levels for grid-forming converters (as defined in [1]), ranging from 5% to 100%. The csv-files, e.g., ABCD_5.csv, contain a 6x6 matrix describing the LTI model in block form [A,B; C, D]. The LTI model has 4 dynamic states (A has dimension ...
    Model
  2. Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks 

    Vogl, Florian; Schütz, Pascal; Postolka, Barbara; et al. (2022)
    Codes and Model for the publication "Personalised pose estimation from single-plane moving fluoroscope images using deep convolutional neural networks"
    Model
  3. 3D heat transfer model 

    Leith, Kerry; Li, Ying (2022)
    In order to evaluate temperature distribution over elements involved in uniaxial compression assembly and subjected to heating via an increase of air temperature within the climate chamber, we set up a 3D heat transfer model in COMSOL. A cylindrical aluminium specimen was constrained by steel loading plates, of which the top and bottom surfaces are set to room temperature as they are in contact with the air outside the climate chamber, ...
    Model
  4. Bio-economic model on benefits of increasing information accuracy in variable rate technologies 

    Späti, Karin; Huber, Robert; Finger, Robert (2021)
    This bio-economic modeling framework assesses the benefits of different sensor approaches to measuring environmental heterogeneity in the field, ranging from the use of satellite imagery to drones and portable N sensors, in variable rate fertilization. The model is described in detail in the following article: Späti, K., Huber, R., & Finger, R. (2021). Benefits of Increasing Information Accuracy in Variable Rate Technologies. Ecological ...
    Model
  5. Supplementary Material of "Impacts of a Revised Surface Roughness Parameterization in the Community Land Model 5.1" 

    Meier, Ronny; Duveiller, Grégory; Davin, Edouard Léopold; et al. (2021)
    The roughness of the land surface (z0) is a key property for the amount of turbulent activity above the land surface and through that for the turbulent exchange of energy, water, omentum, and chemical species between the land and the atmosphere. Variations in z0 are substantial across different types of land cover from typically less than 1 mm over fresh snow or sand deserts up to more than 1 m over urban areas or forests. In this study, ...
    Model
  6. ROMS+NPZD model data (pt.2): additional data 

    Lovecchio, Elisa (2021)
    These data are derived from the same model run and are intended as complementary to the data stored on: https://www.research-collection.ethz.ch/handle/20.500.11850/278536 (doi: https://doi.org/10.3929/ethz-b-000278536) Files contain surface velocities, fluxes at the edge of the euphotic layer, nutrient content and NCP of the euphotic layer (E.L. = 100 m depth).
    Model
  7. BASEMENT v3, a modular freeware for river process modelling: test cases collection 

    Vanzo, Davide; Bürgler, Matthias; Conde, Daniel; et al. (2021)
    Model
  8. Ma2 – Martian Rockfall Convolutional Neural Network 

    Bickel, Valentin (2020)
    Model
  9. M5 – Lunar Rockfall Convolutional Neural Network 

    Bickel, Valentin (2018)
    Model
  10. FARMIND 

    Huber, Robert; Hang, Xiong; Keller, Kevin; et al. (2020)
    This is an agent-based model for the simulation of farm level decision-making developed by the Agricultural Economics and Policy Group at ETH Zurich (www.aecp.ethz.ch). The model represents each farm as an agent as well as a node in social networks. Each agent chooses a strategy for perform activities based on the satisfaction and uncertainty about its output. A set of activity options is determined for each agent according to the strategy ...
    Model
  11. Ensemble model of dispersed hnRNP A1 

    Jeschke, Gunnar (2020)
    Based on the NMR structure of UP1 (PDB 2LYV), the glycine-rich domain (188-320) was modelled using 19 distance distribution restraints. The ensemble model was validated by jack-knife resampling.
    Model
  12. GRID score implementation excel file 

    Kraft, Toni; Roth, Philippe; Wiemer, Stefan (2020)
    This excel file allows to calculate the GRID score for a deep geothermal system as described in Kraft et al. (2020) 'Good Practice Guide for Managing Induced Seismicity in Deep Geothermal Projects in Switzerland' version 2, https://doi.org/10.3929/ethz-b-000453228 .
    Model
  13. LevelDynamics_Matlab/Simulink_R2020a_sources.7z 

    Glattfelder, Adolf Hermann (2020)
    Model

View more