Eldorado - Repository of the TU Dortmund
Resources for and from Research, Teaching and Studying
This is the institutional repository of the TU Dortmund. Ressources for Research, Study and Teaching are archived and made publicly available.
Communities in Eldorado
Select a community to browse its collections.
Recent Submissions
Amtliche Mitteilungen der Technischen Universität Dortmund Nr. 19/2025
(Technische Universität Dortmund, 2025-07-14)
Simulation study to evaluate when Plasmode simulation is superior to parametric simulation in comparing classification methods on high-dimensional data
(2025-06-02) Stolte, Marieke; Schreck, Nicholas; Slynko, Alla; Saadati, Maral; Benner, Alex; Rahnenführer, Jörg; Bommert, Andrea
Simulation studies, especially neutral comparison studies, are crucial for evaluating and comparing statistical methods as they investigate whether methods work as intended and can guide an appropriate method choice. Typically, the term simulation refers to parametric simulation, i.e. computer experiments using pseudo-random numbers. For these, the full data-generating process (DGP) and outcome-generating model (OGM) are known within the simulation. However, the specification of realistic DGPs might be difficult in practice leading to oversimplified assumptions. The problem is more severe for higher-dimensional data as the number of parameters to specify typically increases with the number of variables in the data. Plasmode simulation, which is a combination of resampling covariates from a real-life dataset from the DGP of interest together with a specified OGM is often claimed to solve this problem since no explicit specification of the DGP is necessary. However, this claim is not well supported by empirical results. Here, parametric and Plasmode simulations are compared in the context of a method comparison study for binary classification methods. We focus on studies conducted with some specific data type or application in mind whose true, unknown data-generating mechanism is mimicked. The performance of Plasmode and parametric comparison studies for estimating classifier performance is compared as well as their ability to reproduce the true method ranking. The influence of misspecifications of the DGP on the results of parametric simulation and of misspecifications of the OGM on the results of parametric and Plasmode simulation are investigated. Moreover, different resampling strategies are compared for Plasmode comparison studies. The study finds that misspecifications of the DGP and OGM negatively influence the ability of the comparison studies to estimate the classification performances and method rankings. The best choice of the resampling strategy in Plasmode simulation depends on the concrete scenario.
Towards an Optimal Control Perspective of ResNet Training
(2025) Püttschneider, Jens; Heilig, Simon; Fischer, Asja; Faulwasser, Timm
We propose a training formulation for ResNets reflecting an optimal control problem that is applicable for standard architectures and general loss functions. We suggest bridging both worlds via penalizing intermediate outputs of hidden states corresponding to stage cost terms in optimal control. For standard ResNets, we obtain intermediate outputs by propagating the state through the subsequent skip connections and the output layer. We demonstrate that our training dynamic biases the weights of the unnecessary deeper residual layers to vanish. This indicates the potential for a theory-grounded layer pruning strategy.
Blockchain and additive manufacturing: a taxonomy of business models
(2025-06-10) Grünewald, Alexander; Stuckmann-Blumenstein, Patrick; Keitzl, Patrick; Krämer, Larissa
Additive manufacturing processes such as 3D printing have seen significant progress in the industry in recent years and have become an integral part of Industry 4.0. This fourth industrial revolution is characterized by the increasing networking and automation of production systems and the use of large amounts of data. In this context, distributed ledger technologies (DLT), which include blockchain technology, offer promising opportunities to change production fundamentally. Production processes can be more secure and efficient by creating trust and transparency in data storage and eliminating dependence on centralized instances. However, the full potential of blockchain technology is often not realized due to the perceived complexity of its implementation. Overcoming this skepticism requires a better understanding of the application possibilities and, more importantly, successful practical examples demonstrating blockchain technology’s transformative power in the industry. This study explores how blockchain can be effectively integrated into additive manufacturing processes and offers a structured overview of existing blockchain-based business models within this domain. Hence, a systematic literature interview, Crunchbase review, and Workshop are performed to examine specific use cases of blockchain in additive manufacturing and analyze how these technologies interact with existing business models. In order to provide an overview of existing blockchain-based business models in the context of additive manufacturing, a taxonomy is developed in the underlying paper to identify characteristic features. The taxonomy is further demonstrated along different existing business models.
Sobolev neural network with residual weighting as a surrogate in linear and non-linear mechanics
(2024-09-23) Kilicsoy, Ali Osman Mert; Liedmann, Jan; Valdebenito, Marcos A.; Barthold, Franz-Joseph; Faes, Matthias G. R.
Areas of computational mechanics such as uncertainty quantification and optimization usually involve repeated evaluation of numerical models that represent the behavior of engineering systems. In the case of complex non-linear systems however, these models tend to be expensive to evaluate, making surrogate models quite valuable. Artificial neural networks approximate systems very well by taking advantage of the inherent information of its given training data. In this context, this paper investigates the improvement of the training process by including sensitivity information, which are partial derivatives w.r.t. inputs, as outlined by Sobolev training. In computational mechanics, sensitivities can be applied to neural networks by expanding the training loss function with additional loss terms, thereby improving training convergence resulting in lower generalisation error. This improvement is shown in two examples of linear and non-linear material behavior. More specifically, the Sobolev designed loss function is expanded with residual weights adjusting the effect of each loss on the training step. Residual weighting is the given scaling to the different training data, which in this case are response and sensitivities. These residual weights are optimized by an adaptive scheme, whereby varying objective functions are explored, with some showing improvements in accuracy and precision of the general training convergence.