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Joshi, A. (author), Thakolkaran, P. (author), Zheng, Y. (author), Escande, Maxime (author), Flaschel, Moritz (author), De Lorenzis, Laura (author), Kumar, Siddhant (author)Within the scope of our recent approach for Efficient Unsupervised Constitutive Law Identification and Discovery (EUCLID), we propose an unsupervised Bayesian learning framework for discovery of parsimonious and interpretable constitutive laws with quantifiable uncertainties. As in deterministic EUCLID, we do not resort to stress data, but...journal article 2022
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Cai, Jie (author), Jiang, X. (author), Yang, Yazhou (author), Lodewijks, Gabriel (author), Wang, Minchang (author)A corrosion defect is recognized as one of the most severe phenomena for high-pressure pipelines, especially those served for a long time. Finite-element method and empirical formulas are thereby used for the strength prediction of such pipes with corrosion. However, it is time-consuming for finite-element method and there is a limited...journal article 2022
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Han, Yu (author), Hegyi, A. (author), Zhang, Le (author), He, Zhengbing (author), Chung, Edward (author), Liu, Pan (author)Conventional reinforcement learning (RL) models of variable speed limit (VSL) control systems (and traffic control systems in general) cannot be trained in real traffic process because new control actions are usually explored randomly, which may result in high costs (delays) due to exploration and learning. For this reason, existing RL-based...journal article 2022
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Sharma, Shubham (author), Hari, K.V.S. (author), Leus, G.J.T. (author)Variable density sampling of the k-space in MRI is an integral part of trajectory design. It has been observed that data-driven trajectory design methods provide a better image reconstruction as compared to trajectories obtained from a fixed or a parametric density function. In this paper, a data-driven strategy has been proposed to obtain non...conference paper 2022
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Rath, Suman (author), Zografopoulos, Ioannis (author), Vergara Barrios, P.P. (author), Nikolaidis, Vassilis C. (author), Konstantinou, Charalambos (author)Embedded controllers, sensors, actuators, advanced metering infrastructure, etc. are cornerstone components of cyber-physical energy systems such as microgrids (MGs). Harnessing their monitoring and control functionalities, sophisticated schemes enhancing MG stability can be deployed. However, the deployment of ‘smart’ assets increases the...conference paper 2022
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Lee, Sujin (author), Lee, Jinwoo (author), Hiemstra-van Mastrigt, S. (author), Kim, E.Y. (author)As city-level modal splits are outcomes of city functions, it is essential to understand whether and how city attributes affect modal splits to derive a modal shift toward low-emission travel modes and sustainable mobility in cities. This study elucidates this relationship between modal splits and city attributes in 46 cities worldwide,...journal article 2022
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van Wijk, Robert (author), Lazcano, Andrea Michelle Rios (author), Akutain, Xabier Carrera (author), Shyrokau, B. (author)Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the driver's intention, resulting in unnatural guidance and low customer acceptance. In this research, we focus on a novel data-driven approach to predict driver steering torque. In particular, driver behavior is modeled by learning the parameters of a...journal article 2022
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Valchev, I. (author), Coraddu, A. (author), Oneto, L. (author), Kalikatzarakis, M. (author), Tiddens, W. (author), Geertsma, R.D. (author)Deterministic models based on the laws of physics, as well as data-driven models, are often used to assess the current state of vessels and their systems, as well as predict their future behaviour. Predictive maintenance methodologies (i.e., Condition Based Maintenance) and advanced control strategies (i.e., Model Predictive Control) are...journal article 2022
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Belardinelli, P. (author), Chandrashekar, A. (author), Wiebe, R. (author), Alijani, F. (author), Lenci, S. (author)Modal interactions are pervasive effects that commonly emerge in nanomechanical systems. The coupling of vibrating modes can be leveraged in many ways, including to enhance sensing or to disclose complex phenomenologies. In this work we show how machine learning and data-driven approaches could be used to capture intermodal coupling. We...journal article 2022
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de Bruijn, J.A. (author), Warnier, Martijn (author), Janssen, M.F.W.H.A. (author)Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a solution for automated decisions, the wicked nature of the challenges governments face complicates the use of XAI. Wickedness means that the facts that define...journal article 2022
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Zhao, Jian (author), Xu, Shenyu (author), Chandrasegaran, R.S.K. (author), Bryan, Christopher James (author), Du, Fan (author), Mishra, Aditi (author), Qian, Xin (author), Li, Yiran (author), Ma, Kwan Liu (author)Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack...journal article 2022
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Weymouth, Gabriel D. (author)Pressure projection is the single most computationally expensive step in an unsteady incompressible fluid simulation. This work demonstrates the ability of data-driven methods to accelerate the approximate solution of the Poisson equation at the heart of pressure projection. Geometric Multi-Grid methods are identified as linear convolutional...journal article 2022
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Nadi Najafabadi, A. (author), Sharma, Salil (author), van Lint, J.W.C. (author), Tavasszy, Lorant (author), Snelder, M. (author)This paper proposes a data-driven transport modeling framework to assess the impact of freight departure time shift policies. We develop and apply the framework around the case of the port of Rotterdam. Container transport demand data and traffic data from the surrounding network are used as inputs. The model is based on a graph convolutional...journal article 2022
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Zhang, Liang (author), Chen, Pengfei (author), Li, M. (author), Chen, Linying (author), Mou, Junmin (author)The consequences caused by bridge failures owing to the ship-bridge collision are always severe in terms of loss of life, economy, and environmental consequences to individuals and societies. The previous studies focused on the ship-bridge collision mainly concentrated on passive anti-collision, such as strengthening the bridge structure or...journal article 2022
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Li, D. (author), De Schutter, B.H.K. (author)Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...journal article 2022
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Aarnoudse, Leontine (author), Oomen, T.A.E. (author)Parameterized feedforward control is at the basis of many successful control applications with varying references. The aim of this paper is to develop an efficient data-driven approach to learn the feedforward parameters for MIMO systems. To this end, a cost criterion is minimized using a stochastic gradient descent algorithm, in which both...journal article 2022
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Huang, Yilin (author), Xie, Xu (author), Cho, Yubin (author), Verbraeck, A. (author)Data assimilation (DA) is a methodology widely used by different disciplines of science and engineering. It is typically applied to continuous systems with numerical models. The application of DA to discrete-event and discrete-time systems including agent-based models is relatively new. Because of its non-linearity and non-Gaussianity, the...journal article 2022
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Coraddu, A. (author), Kalikatzarakis, Miltiadis (author), Theotokatos, Gerasimos (author), Geertsma, R.D. (author), Oneto, Luca (author)Accurate, reliable, and computationally inexpensive models of the dynamic state of combustion engines are a fundamental tool to investigate new engine designs, develop optimal control strategies, and monitor their performance. The use of those models would allow to improve the engine cost-efficiency trade-off, operational robustness, and...book chapter 2022
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Forouzandeh Shahraki, N. (author), Zomorodian, Zahra Sadat (author), Tahsildoost, Mohammad (author), Shaghaghian, Zohreh (author)Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single...journal article 2022
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Nguyen, Viet Anh (author), Kuhn, Daniel (author), Mohajerin Esfahani, P. (author)We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst case (maximum) of Stein’s loss across all normal reference distributions within a...journal article 2022