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Data-Centric Engineering @DCE_Journal

Data-Centric Engineering, a new #openaccess journal at the interface of engineering and data science. Published by @CambridgeUP and supported by @LR_Foundation.

Joined June 2019
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646 Photos and videos
New article! AI-driven underwater 3D scanning: enhancing efficiency and accuracy with Bayesian optimization and Gaussian process regression 👉cup.org/4v2DfOq ✍️Benjamin Williams, @autogeolab, Karen Donaldson and James Minto #AI #Bayesian #Gaussian #underwaterscanning
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New article! Sparse surface pressure-based reconstruction of the flow around a thick airfoil over a range of angles of attack 👉 cup.org/4o8qjnu By Quentin Bucquet, Bérengère Podvin, Caroline Braud & Emmanuel Guilmineau #windenergy #data
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Recently published! Predicting crack evolution for small- and medium-span beam bridges using XGBoost–SHAP model 👉 cup.org/4eVKRgq By Dong Liang, Mingjian Huang, Haibin Huang, Fangdian Di, Zhiyi Zhang and Jing Shi #bridge #XGBoost #SHAPalgorithm

Predicting crack evolution for small- and medium-span beam bridges using XGBoost–SHAP model |...

Predicting crack evolution for small- and medium-span beam bridges using XGBoost–SHAP model - Volume 7

cambridge.org
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New article! A data-driven assessment of buckling strength reduction in truncated conical shells: development of a hybrid Gaussian process-XG boost machine learning framework 👉 cup.org/4nCOsSA ✍️Rohan Majumder, Budhaditya De, Aman Deep Gupta & Sudib Kumar Mishra #data

A data-driven assessment of buckling strength reduction in truncated conical shells: development of...

A data-driven assessment of buckling strength reduction in truncated conical shells: development of a hybrid Gaussian process-XG boost machine learning framework - Volume 7

cambridge.org
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New article! Digital twin for virtual sensing of ferry quays via a Gaussian process latent force model 👉 cup.org/3OJYnck By Luigi Sibille, Torodd Skjerve Nord and @ADvulab #digitaltwin #virtualsensing #Gaussian #structuralhealthmonitoring

Digital twin for virtual sensing of ferry quays via a Gaussian process latent force model |...

Digital twin for virtual sensing of ferry quays via a Gaussian process latent force model - Volume 7

cambridge.org
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New article! Leveraging scale separation and stochastic closure for data-driven prediction of chaotic dynamics 👉 cup.org/4tGKjiW By Ismaël Zighed, Nicolas Thome, Patrick Gallinari & Taraneh Sayadi #data #dynamics #turbulence #probabilisticmodeling #stochasticmodeling
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New article! Making global sensitivity analysis feasible using neural network surrogates 👉 bit.ly/48DmfUJ By Gihan Weerasinghe, @ramaseshan_k and Samila Bandara (@Arup) #data #neuralnetwork #surrogatemodels #geotechnicalanalysis #engineering
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Today's the Day! 📺 DCE WEBINAR: No Equations, No Variables, No Space and No Time: Data and the Modeling of Complex Systems with Ioannis Kevrekidis (@JohnsHopkins) ⏰ 1100 ET (US & Canada) | 1600 UK | 1700 CET ℹ️ bit.ly/3XFAALo #data #neural #networks
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📺 DCE WEBINAR: No Equations, No Variables, No Space and No Time: Data and the Modeling of Complex Systemswith Ioannis Kevrekidis (@JohnsHopkins) 📅 Wednesday 10 December ⏰ 1100 ET (US & Canada) | 1600 UK | 1700 CET ℹ️ bit.ly/3XFAALo #data #neural #networks
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New article! Projection-based model-order reduction via graph autoencoders suited for unstructured meshes 👉 bit.ly/44vljAv By @liam_magargal, Parisa Khodabakhshi, Steven Rodriguez, @JaworskiFSI and John Michopoulos #DeepLearning #GraphAutoencoders
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New article! Interpretable and efficient data-driven discovery and control of distributed systems 👉bit.ly/449T0HJ By Florian Wolf, Nicolò Botteghi, Urban Fasel & Andrea Manzoni Part of Physics-Enhancing #MachineLearning special collection #autoencoder #data
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New article! Adversarial disentanglement by backpropagation with physics-informed variational autoencoder 👉 bit.ly/4p6PRAo By Ioannis Christoforos Koune (@tudelft) & @ADvulab (@Cambridge_Eng) #generativemodels #MachineLearning #Physics
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📢 Call for Papers: AI in Structural Engineering 🏗️ 🌆 DCE is collaborating with the recent #ARTISTE2025 Conference on a special collection that explores advances and possibilities for #AI in #structural #engineering. 🗓️Deadline: 1 Sep 2026 ℹ️Info: bit.ly/48E0lTd
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New article! Evaluating and benchmarking data-driven performance forecasting techniques for infrastructure asset management 👉 bit.ly/3WbCUJk By Ze Zhou Wang, Zhaojie Sun, Abir Al-Tabbaa & Bachar Hakim (@Cambridge_Eng) #data #MachineLearning #engineering
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Recently published! Mean flow data assimilation using physics-constrained graph neural networks 👉 bit.ly/4pWjZzH by Michele Quattromini (@PolibaOfficial), Michele Alessandro Bucci (@SAFRAN), Stefania Cherubini & Onofrio Semeraro #MachineLearning #surrogatemodelling
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New article! Leveraging rules on integrated 3D semantic city models for validating scenarios of urban evolution 👉 bit.ly/4pXMa1j By Diego Vinasco-Alvarez, John Samuel, Sylvie Servigne and Gilles Gesquière (@insadelyon) #data #validation #semanticweb
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📢 Call for Papers: AI in Structural Engineering 🏗️ 🏨 🌆 DCE is collaborating with the recent #ARTISTE2025 Conference on a special collection that explores advances and possibilities for #AI in #structural #engineering. 🗓️ Deadline: 1 Sep 2026 ℹ️ Info: bit.ly/48E0lTd
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New article! Using graph neural networks and frequency domain data for automated operational modal analysis of populations of structures 👉 bit.ly/4mFGu9e By Xudong Jian, Yutong Xia, @gduthe_, Kiran Bacsa, Wei Liu and @Lne_Chatzi #DeepLearning #GraphNeuralNetworks
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Did you miss our webinar on Transmitted Variation in Engineering Design with @tdavis5? Click for a recording of this informative event, revisiting & expanding upon Jim Morrison's influential 1957 work on transmitted variation in #engineering #design 🎥 bit.ly/46oSN5d
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Also check out @tdavis5's earlier article for the journal for an engaging read on this topic Free to read, share and download here: bit.ly/42q40jr #robustness #parameterdesign #transmittedvariation #statisticalengineering #engineering

The study of variability in engineering design—An appreciation and a retrospective | Data-Centric...

The study of variability in engineering design—An appreciation and a retrospective - Volume 2

cambridge.org
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Recently published! Building Information Graphs (BIGs): remodeling building information for learning and applications 👉 bit.ly/4nqlQLr By @w_zijian & @rafael_sacks Find out why BIGs represent a paradigm shift in design technologies that bridge #AI & building design
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