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    Item type:Research Data, Access status: without files ,
    Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals - Dataset
    (2021-01-27)
    Kastner, Marvin
    ;
    Nellen, Nicole
    ;
    Schwientek, Anne Kathrina
    ;
    Jahn, Carlos
    At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics - Tree-structured Parzen Estimator, Bayesian Optimization, Simulated Annealing, and Random Search - guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable. The dataset is stored in the Excel file named Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals - Dataset.xlsx.This contains the data to create the figures and the data to run the statistics. The data analysis is stored in the Jupyter Notebook named Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals - Data Analysis.ipynb. This contains tje code to create the figures and the data to run the statistics.
    Data Type: Dataset
      12
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    UDEsAnalysis
    (2026-04-07)
    Sommer, Finn Luca
    First release of analysis tools for the universal differential equation (UDE) framework used to approximate the Basset force. This repository contains the software accompanying the preprint: Sommer, Rathi, Götschel, Ruprecht, "Approximation of the Basset force in the Maxey–Riley–Gatignol equations via universal differential equations".
    Data Type: Research Software
      18
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    Parallel-in-Time/pySDC
    (2024-09-23)
    Speck, Robert
    ;
    Lunet, Thibaut
    ;
    Baumann, Thomas
    ;
    Wimmer, Lisa
    ;
    Akramov, Ikrom
    The pySDC project is a Python implementation of the spectral deferred correction (SDC) approach and its flavors, esp. the multilevel extension MLSDC and PFASST. It is intended for rapid prototyping and educational purposes. New ideas like e.g. sweepers or predictors can be tested and first toy problems can be easily implemented.
    Data Type: Research Software
      22
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    Data for Paper: Optimizing Temperature, Pressure, and Waste Heat Utilization in PEM Electrolyzers: A Model-based Approach to Enhance Integrated Energy System Efficiency
    (2025-03-12)
    Bornemann, Luka
    Data and code to reproduce results and figures presented in the paper "Optimizing Temperature, Pressure, and Waste Heat Utilization in PEM Electrolyzers: A Model-based Approach to Enhance Integrated Energy System Efficiency" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, submitted for The 38th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems – ECOS 2025. The code consists of: - Plotting the results figures (plotting.py)
    Data Type: Research Software ; Data Type: Dataset
      17
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    WIR - The WCC Compiler Intermediate Representation
    (2026-08-25)
    Falk, Heiko
    This package contains the WCC Intermediate Representation framework, WIR for short.
    Data Type: Source Code
      6
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    Code and Data for Paper: Oxygen Production via Electrolysis: A Model-Based Assessment of Its Impact on a Climate-Neutral German Energy System
    (2025-04-07)
    Bornemann, Luka
    Code and data to reproduce results and figures presented in the paper "Oxygen Production via Electrolysis: A Model-Based Assessment of Its Impact on a Climate-Neutral German Energy Systems" by Luka Bornemann and Jelto Lange and Martin Kaltschmitt, published in Energy Conversion and Management. The code contains the entire structure of the open source capacity expansion model PyPSA-Eur (v 0.11) including the corresponding extensions of the oxygen value chain. The results of each scenario can be found within the 'results' directory.
    Data Type: Research Software
      29
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    Code and Data for Evaluating Large Language Models for Design Validation
    (2024-07-05)
    Rahman, Abdur
    ;
    Fey, Görschwin
    Evaluated several prominent Large Language Models on their ability to recognize the design and any faults or issues present in the design from design descriptions provided in VHDL and their ability to produce appropriate test benches for them.
    Data Type: Source Code
      21
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    LIBUSEFUL - A Collection of Useful Routines and Classes for Compiler Development and Debugging
    (2026-08-26)
    Falk, Heiko
    This package contains the USEFUL library, a collection of useful routines and classes for compiler development and debugging.
    Data Type: Source Code
      5
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    In-silico Lagrangian sensor tracking in a stirred tank reactor: filtering code
    (2026-06-10)
    Rathi, Vamika
    This repository contains the code used for tracking in-silico Lagrangian sensors governed by the Maxey–Riley–Gatignol equations, as presented in the paper (preprint): Rathi, Sehar, Sommer, Götschel, Stuewe, von Kameke, Ruprecht, "Tracking in-silico Lagrangian sensors in a lab-scale stirred tank reactor", preprint, tba. The repository includes: filtering algorithms and scripts file for interpolation objects for experimental velocity field the Maxey–Riley–Gatignol forward dynamics with analytic Jacobians analytical and experimental velocity-field providers utilities for measurements, noise and plotting a central .json configuration file for all particle, fluid, and filter parameters tracking results and figures Using this code, it is possible to reproduce all results discussed in the paper.
    Data Type: Source Code
      49
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    Numerical Methods for the Maxey-Riley-Gatignol Equations
    (2026-04-08)
    Sommer, Finn
    First release of a trajectory generation framework for training universal differential equations (UDEs) to approximate the Basset (history) force in the Maxey–Riley–Gatignol equations. This repository provides trajectory generation based on the full Maxey–Riley–Gatignol equations for two types of fluid fields: a synthetic vortex field and an experimental field derived from interpolated measurement data. The interpolation routines are included as part of the codebase. The implementation is available in both Python and Julia. This repository contains the software accompanying the preprint: Sommer, Rathi, Götschel, Ruprecht, “Approximation of the Basset force in the Maxey–Riley–Gatignol equations via universal differential equations”, preprint, tba.
    Data Type: Source Code
      26