Srinivasan, V., Song, B., Luo, J.
, Karupppasamy, S., Elara Mohan, R., Blessing, L. & Wood, K. (2023).
Do Analogies and Analogical Distance Influence Ideation Outcomes in Engineering Design? I S. Mukherjee, V. Dutt & N. Srinivasan (red.),
Applied Cognitive Science and Technology: Implications of Interactions Between Human Cognition and Technology (s. 211–230). Springer.
https://doi.org/10.1007/978-981-99-3966-4_13Fulco, S.
, Budzik, M. K., Xiao, H., Durian, D. J. & Turner, K. T. (2025).
Disorder enhances the fracture toughness of 2D mechanical metamaterials.
PNAS Nexus,
4(2), Artikel pgaf023.
https://doi.org/10.1093/pnasnexus/pgaf023Dideriksen, A. K., Andersen, M. F., Priest, J., Eriksen, N. F., Frandsen, M. T.
, Melvad, C., Rasmussen, T. F., Nielsen, N. H. K., Strømsnes, C. T., Ahlebæk, M. J., Samsing, S., Larsen, T. B.
, Don, J., Pedersen, L. A. N., Jacobsen, R. H., Rysgaard, S., Kim, J. M., Bayer, R., Christensen, C.
... Karoff, C. (2024).
DISCO-2 – an ambitious earth observing student CubeSat for arctic climate research.
Frontiers in Remote Sensing,
5, Artikel 1474560.
https://doi.org/10.3389/frsen.2024.1474560
Jensen, A. M. D., Schoerghofer-Queiroz, A.
, Ulriksen, M. D., Tcherniak, D.
, Damkilde, L., Talasila, P., Larsen, P. G. & Abbiati, G. (2024).
Digital twin as a service for damage prognosis of offshore wind turbine foundations. I W. Desmet, B. Pluymers, D. Moens & J. del Fresno Zarza (red.),
Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics (s. 4127-4141). KU Leuven, Departement Werktuigkunde.
Feng, H., Gomes, C., Sandberg, M., Thule, C., Lausdahl, K. & Larsen, P. G. (2021).
Developing a Physical and Digital Twin: An Example Process Model. I
Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021 (s. 286-295). IEEE.
https://doi.org/10.1109/MODELS-C53483.2021.00050
D. Huang, X. L. ., Jain, N.
, Abkar, M., Kunz, R. F. & Yang, X. I. A. (2021).
Determining a priori a RANS model’s applicable range via global epistemic uncertainty quantification.
Computers & Fluids,
230, Artikel 105113.
https://doi.org/10.1016/j.compfluid.2021.105113Djeffal, S., Ghoul, A., Saadi, A., Izri, Z., Morakchi, M. R. & Wang, H. (2025).
Design optimization and simulation of a 3D printed cable-driven continuum robot using IKM-ANN and nTop software.
Proceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering,
239(2), 200-218.
https://doi.org/10.1177/09596518241276233He, L., Wang, C., Zhang, M.
, Li, J., Chen, T. & Zhou, X. (2025).
Design of BCC/FCC dual-solid solution refractory high-entropy alloys through CALPHAD, machine learning and experimental methods.
npj Computational Materials,
11(1), Artikel 105.
https://doi.org/10.1038/s41524-025-01597-3Si, G., Sun, L., Zhang, Z.
& Zhang, X. (2021).
Design, Fabrication, and Testing of a Novel 3D 3-Fingered Electrothermal Microgripper with Multiple Degrees of Freedom.
Micromachines,
12(4), Artikel 444.
https://doi.org/10.3390/mi12040444Cristóbal, A. B., Sanz-Cuadrado, C.
, Zhang, Z., Victoria, M., Fialho, L., Cavaco, A., Bokalič, M. & Narvarte, L. (2025).
Delving into the modeling and operation of energy communities as epicenters for systemic transformations.
Universal Access in the Information Society,
24(3), 2007-2023.
https://doi.org/10.1007/s10209-023-01056-0Hudert, M. M., Elvebakken, M. F., Meagher, M.
, Mangliar, L., Zhang, X. & Esterle, L. (2022).
Deep learning enhanced robotic fabrication of timber-to-timber connections with densified hardwood nails. I
Proceedings of the IASS 2022 Symposium affiliated with APCS 2022 conference: Innovation - Sustainability - Legacy (s. 1740-1748)
Airao, J., Fattahi, S.
, Truong, T. T., Alinaghizadeh, A., Azarhoushang, B.
, Karras, P. & Aghababaei, R. (2026).
Data driven tool wear and remaining useful tool life estimation.
Wear,
600, Artikel 206808.
https://doi.org/10.1016/j.wear.2026.206808
Amarloo, A., Cinnella, P.
, Iosifidis, A., Forooghi, P. & Abkar, M. (2023).
Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector.
Physics of Fluids,
35(7), Artikel 075154.
https://doi.org/10.1063/5.0160977
Eidi, A.
, Zehtabiyan-Rezaie, N., Chiassi, R., Yang, X. I. A.
& Abkar, M. (2022).
Data-driven quantification of model-form uncertainty in Reynolds-averaged simulations of wind farms.
Physics of Fluids,
34(8), Artikel 085135.
https://doi.org/10.1063/5.0100076Truong, T. T., Airao, J., Hojati, F., Ilvig, C. F., Azarhoushang, B.
, Karras, P. & Aghababaei, R. (2024).
Data-driven prediction of tool wear using Bayesian regularized artificial neural networks.
Measurement: Journal of the International Measurement Confederation,
238, Artikel 115303.
https://doi.org/10.1016/j.measurement.2024.115303
Böttjer, T., Ørnskov Rønsch, G.
, Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021).
Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.),
Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconfigurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer.
https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878
