Eldorado - Repository of the TU Dortmund
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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.
Classic statistical and modern machine learning methods for modeling and prediction of major tennis tournaments
(2025) Buhamra, Nourah; Groll, Andreas; Pauly, Markus
The cumulative dissertation proposes a comprehensive approach to predicting outcomes in Grand Slam tennis tournaments, focusing on the probability that the first-named player will win. Our study incorporates several classical regression and machine learning models, evaluated using cross-validation and external validation through performance measures such as classification rate, predictive likelihood, and Brier score. Two specific aspects are examined in greater detail: non-linear effects and the inclusion of additional player and court-specific abilities. Moreover, we analyze the predictive potential of statistically enhanced covariates and apply procedures from the field of interpretable machine learning to make complex models more understandable. Our analyses show that in predicting Grand Slam tennis matches, while there are slight differences across various statistical and machine learning approaches, the specific forecasting strategy used plays an even more critical role. Additionally, the results confirm that enhanced variables contribute positively to model performance and provide deeper insights into predictors of match outcomes in sports analytics.
Entwicklung von Machine Learning basierten Materialmodellen für Finite-Elemente-Simulationen
(2024) Böhringer, Pauline; Rudolph, Günter; Wiederkehr, Petra
Finite-Elemente-Simulationen sind essenziell für die Strukturanalyse mechanischer Komponenten und finden Anwendung in Bereichen wie Umformprozessen und Crashtests. Die Genauigkeit solcher Simulationen hängt stark von den eingesetzten Materialmodellen ab, deren Erstellung jedoch komplex ist. In dieser Arbeit wird untersucht, ob klassische Materialmodelle durch datenbasierte Modelle mittels maschinellen Lernens (ML) ersetzt werden können. Dazu werden verschiedene ML-Modelle mithilfe zufällig generierter Daten aus klassischen Materialmodellen trainiert und hinsichtlich ihrer Eignung bewertet. Im zweiten Teil wird ein Ansatz vorgestellt, ML-Modelle direkt mittels Daten aus Versuchen, ohne ein klassisches Referenzmodell zu trainieren, wobei physikalisch motivierte Gleichungen für das Training genutzt werden. Der Fokus liegt auf der Anpassung und Evaluierung unterschiedlicher Trainingsmethoden für die ML-Materialmodelle.