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Kiste, Amund (author)Solving Partial Differential Equations (PDEs) in engineering such as Navier-Stokes is incredibly computationally expensive and complex. Without analytical solutions, numerical solutions can take ages to simulate at great expense. In order to reduce this cost, neural networks may be used to compute approximations of the solution for use during...bachelor thesis 2024
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Taklimi, Sam (author)The objective of this project is to train a model that transforms a tree with its foliage into only its branch structure. This is achieved by employing machine-learning techniques, specifically Generative Adverserial Networks (GANs). By utilizing the proposed method, a predictive model is built that automatically minimizes its own error function...bachelor thesis 2024
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Lacombe, Pablo (author)This paper presents a comprehensive exploration of a novel method combining Principal Component Analysis (PCA) and Neural Networks (NN) to efficiently solve Partial Differential Equations (PDEs), a fundamental challenge in modeling a wide range of real-world phenomena. Our research extends the work of Bhattacharya et al. by focusing on PCA for...bachelor thesis 2024
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Hueber, Paul (author)Designing processors for implantable closed-loop neuromodulation systems presents a formidable challenge owing to the constrained operational environment, which requires low latency and high energy efficacy. Previous benchmarks have provided limited insights into power consumption and latency. However, this study introduces algorithmic metrics...master thesis 2024
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Sebus, Siert (author)The Deep Neural Network (DNN) has become a widely popular machine learning architecture thanks to its ability to learn complex behaviors from data. Standard learning strategies for DNNs however rely on the availability of large, labeled datasets. Self-Supervised Learning (SSL) is a style of learning that allows models to also use unlabeled data...master thesis 2024
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Schweidtmann, A.M. (author), Zhang, Dongda (author), von Stosch, Moritz (author)The term hybrid modeling refers to the combination of parametric models (typically derived from knowledge about the system) and nonparametric models (typically deduced from data). Despite more than 20 years of research, over 150 scientific publications (Agharafeie et al., 2023), and some recent industrial applications on this topic, the...review 2024
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Safaei, Mohammad (author), Hejazian, Mahsa (author), Pedrammehr, Siamak (author), Pakzad, Sajjad (author), Ettefagh, Mir Mohammad (author), Fotouhi, M. (author)Gantry cranes play a pivotal role in various industrial applications, and their reliable operation is paramount. While routine inspections are standard practice, certain defects, particularly in less accessible components, remain challenging to detect early. In this study, first a finite element model is presented, and the damage is introduced...journal article 2024
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Mirkhalaf, Mohsen (author), Rocha, I.B.C.M. (author)During the last few decades, industries such as aerospace and wind energy (among others) have been remarkably influenced by the introduction of high-performance composites. One challenge, however, for modeling and designing composites is the lack of computational efficiency of accurate high-fidelity models. For design purposes, using...journal article 2024
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Kapoor, T. (author), Wang, H. (author), Nunez, Alfredo (author), Dollevoet, R.P.B.J. (author)This paper proposes a novel framework for simulating the dynamics of beams on elastic foundations. Specifically, partial differential equations modeling Euler–Bernoulli and Timoshenko beams on the Winkler foundation are simulated using a causal physics-informed neural network (PINN) coupled with transfer learning. Conventional PINNs encounter...journal article 2024
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Aarnoudse, Leontine (author), Kon, Johan (author), Ohnishi, Wataru (author), Poot, Maurice (author), Tacx, Paul (author), Strijbosch, Nard (author), Oomen, T.A.E. (author)The performance of feedforward control depends strongly on its ability to compensate for reproducible disturbances. The aim of this paper is to develop a systematic framework for artificial neural networks (ANN) for feedforward control. The method involves three aspects: a new criterion that emphasizes the closed-loop control objective,...journal article 2024
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Trunz, Elena (author), Klein, Jonathan (author), Müller, Jan (author), Bode, Lukas (author), Sarlette, Ralf (author), Weinmann, M. (author), Klein, Reinhard (author)We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples. While directly inferring all parameters of the underlying yarn model based on a single neural network may seem an intuitive choice, we show that the complexity...journal article 2024
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He, K. (author), Shi, S. (author), van den Boom, A.J.J. (author), De Schutter, B.H.K. (author)Approximate dynamic programming (ADP) faces challenges in dealing with constraints in control problems. Model predictive control (MPC) is, in comparison, well-known for its accommodation of constraints and stability guarantees, although its computation is sometimes prohibitive. This paper introduces an approach combining the two methodologies...journal article 2024
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Llasag Rosero, Raúl (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information....journal article 2024
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Négyesi, B. (author), Andersson, Kristoffer (author), Oosterlee, Cornelis W. (author)A novel discretization is presented for decoupled forward–backward stochastic differential equations (FBSDE) with differentiable coefficients, simultaneously solving the BSDE and its Malliavin sensitivity problem. The control process is estimated by the corresponding linear BSDE driving the trajectories of the Malliavin derivatives of the...journal article 2024
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Vastl, Martin (author), Kulhanek, Jonas (author), Kubalik, Jiri (author), Derner, Erik (author), Babuska, R. (author)Many real-world systems can be naturally described by mathematical formulas. The task of automatically constructing formulas to fit observed data is called symbolic regression. Evolutionary methods such as genetic programming have been commonly used to solve symbolic regression tasks, but they have significant drawbacks, such as high...journal article 2024
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Mateo-Barcos, S. (author), Ribo-Perez, D.G. (author), Rodríguez-García, J. (author), Alcázar-Ortega, M. (author)This study develops a methodology to characterise and forecast large consumers’ electricity demand, particularly municipalities, with hundreds of different metered supply points based on the previous characterisation of facilities’ consumption. Demand forecasting allows consumers to improve their participation in electricity markets and...journal article 2024
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McDonald, Tom (author), Tsay, Calvin (author), Schweidtmann, A.M. (author), Yorke-Smith, N. (author)ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems. However, previous works are mostly limited to MLPs. Graph neural networks (GNNs) can learn from non-euclidean data...journal article 2024
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Dolanyi, Mihaly (author), Bruninx, K. (author), Toubeau, Jean Francois (author), Delarue, Erik (author)In competitive electricity markets, the optimal bid or offer problem of a strategic agent is commonly formulated as a bi-level program and solved as a mathematical program with equilibrium constraints (MPEC). If the lower-level (LL) part of the problem can be well approximated as a convex problem, this approach leads to a global optimum. However...journal article 2024
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Hu, M. (author), Yue, N. (author), Groves, R.M. (author)With the increasing application of artificial intelligence (AI) techniques in the field of structural health monitoring (SHM), there is a growing interest in explaining the decision-making of the black-box models in deep learning-based SHM methods. In this work, we take explainability a step further by using it to improve the performance of...journal article 2024
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Hu, M. (author), Yue, N. (author), Groves, R.M. (author)With the improvements in computational power and advances in chip and sensor technology, the applications of machine learning (ML) technologies in structural health monitoring (SHM) are increasing rapidly. Compared with traditional methods, deep learning based SHM (Deep SHM) methods are more efficient and have a higher accuracy. However, due...journal article 2024