Data-Centric Engineering

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

[Data-Centric Engineering @DCE_Journal]

New article!

Sparse surface pressure-based reconstruction of the flow around a thick airfoil over a range of angles of attack

👉 https://t.co/Cyu02UAM9r

By Quentin Bucquet, Bérengère Podvin, Caroline Braud & Emmanuel Guilmineau

#windenergy #data

[Data-Centric Engineering @DCE_Journal]

Recently published!

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

👉 https://t.co/7cnOgtuhar

By Dong Liang, Mingjian Huang, Haibin Huang, Fangdian Di, Zhiyi Zhang and Jing Shi

#bridge #XGBoost #SHAPalgorithm

[Data-Centric Engineering @DCE_Journal]

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

👉 https://t.co/jxllaCT1Aw
✍️Rohan Majumder, Budhaditya De, Aman Deep Gupta & Sudib Kumar Mishra

#data

[Data-Centric Engineering @DCE_Journal]

New article!

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

👉 https://t.co/qERrSmOb7f

By Luigi Sibille, Torodd Skjerve Nord and @ADvulab

#digitaltwin #virtualsensing #Gaussian #structuralhealthmonitoring

[Data-Centric Engineering @DCE_Journal]

New article!

Leveraging scale separation and stochastic closure for data-driven prediction of chaotic dynamics

👉 https://t.co/6kjYGrtq2R

By Ismaël Zighed, Nicolas Thome, Patrick Gallinari &
Taraneh Sayadi

#data #dynamics #turbulence #probabilisticmodeling #stochasticmodeling

[Data-Centric Engineering @DCE_Journal]

New article!

Making global sensitivity analysis feasible using neural network surrogates

👉 https://t.co/Pttji5SWjW

By Gihan Weerasinghe, @ramaseshan_k and
Samila Bandara (@Arup)

#data #neuralnetwork #surrogatemodels #geotechnicalanalysis #engineering

[Data-Centric Engineering @DCE_Journal]

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 https://t.co/CyFU51Ihdm

#data #neural #networks

[Data-Centric Engineering @DCE_Journal]

📺 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 https://t.co/CyFU51Ihdm

#data #neural #networks

[Data-Centric Engineering @DCE_Journal]

New article!

Projection-based model-order reduction via graph autoencoders suited for unstructured meshes

👉 https://t.co/hzfryVhMRX

By @liam_magargal, Parisa Khodabakhshi, Steven Rodriguez, @JaworskiFSI and John Michopoulos

#DeepLearning #GraphAutoencoders

[Data-Centric Engineering @DCE_Journal]

New article!

Interpretable and efficient data-driven discovery and control of distributed systems

👉https://t.co/vM1qfmunLB

By Florian Wolf, Nicolò Botteghi, Urban Fasel &
Andrea Manzoni

Part of Physics-Enhancing #MachineLearning special collection

#autoencoder #data

[Data-Centric Engineering @DCE_Journal]

New article!

Adversarial disentanglement by backpropagation with physics-informed variational autoencoder

👉 https://t.co/MVyp6jJgnQ

By Ioannis Christoforos Koune (@tudelft) & @ADvulab (@Cambridge_Eng)

#generativemodels #MachineLearning #Physics

[Data-Centric Engineering @DCE_Journal]

📢 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: https://t.co/dit9M5v00s

[Data-Centric Engineering @DCE_Journal]

New article!

Evaluating and benchmarking data-driven performance forecasting techniques for infrastructure asset management

👉 https://t.co/hlgtylevUA

By Ze Zhou Wang, Zhaojie Sun, Abir Al-Tabbaa & Bachar Hakim (@Cambridge_Eng)

#data #MachineLearning #engineering

[Data-Centric Engineering @DCE_Journal]

Recently published!

Mean flow data assimilation using physics-constrained graph neural networks

👉 https://t.co/36FPrfOOLq

by Michele Quattromini (@PolibaOfficial), Michele Alessandro Bucci (@SAFRAN), Stefania Cherubini & Onofrio Semeraro

#MachineLearning #surrogatemodelling

[Data-Centric Engineering @DCE_Journal]

New article!

Leveraging rules on integrated 3D semantic city models for validating scenarios of urban evolution

👉 https://t.co/0HCy7Q7pyn

By Diego Vinasco-Alvarez, John Samuel, Sylvie Servigne and Gilles Gesquière (@insadelyon)

#data #validation #semanticweb

[Data-Centric Engineering @DCE_Journal]

📢 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: https://t.co/dit9M5v00s

[Data-Centric Engineering @DCE_Journal]

New article!

Using graph neural networks and frequency domain data for automated operational modal analysis of populations of structures

👉 https://t.co/BCV5tx34Lm

By Xudong Jian, Yutong Xia, @gduthe_, Kiran Bacsa, Wei Liu and @Lne_Chatzi

#DeepLearning #GraphNeuralNetworks

[Data-Centric Engineering @DCE_Journal]

Also check out @tdavis5's earlier article for the journal for an engaging read on this topic

Free to read, share and download here: https://t.co/ylKOfnSvhE

#robustness #parameterdesign #transmittedvariation #statisticalengineering #engineering

[Data-Centric Engineering @DCE_Journal]

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

🎥 https://t.co/ROvTXP5a3r

[Data-Centric Engineering @DCE_Journal]

Recently published!

Building Information Graphs (BIGs): remodeling building information for learning and applications

👉 https://t.co/Cg4OvubOGU

By @w_zijian & @rafael_sacks

Find out why BIGs represent a paradigm shift in design technologies that bridge #AI & building design