| Source term inversion of short-lived nuclides in complex nuclear accidents based on machine learning using off-site gamma dose rate
Ling, Y., Liu, C., Shan, Q., Hei, D., Zhang, X., Shi, C., … Wang, J. (2024). Source term inversion of short-lived nuclides in complex nuclear accidents based on machine learning using off-site gamma dose rate. Journal of Hazardous Materials, 465, 133388 (16 pp.). https://doi.org/10.1016/j.jhazmat.2023.133388 |
| Enhanced deep-learning model for carbon footprints of chemicals
Zhang, D., Wang, Z., Oberschelp, C., Bradford, E., & Hellweg, S. (2024). Enhanced deep-learning model for carbon footprints of chemicals. ACS Sustainable Chemistry and Engineering, 12(7), 2700-2708. https://doi.org/10.1021/acssuschemeng.3c07038 |
| Label-free digital holotomography reveals ibuprofen-induced morphological changes to red blood cells
Bergaglio, T., Bhattacharya, S., Thompson, D., & Nirmalraj, P. N. (2023). Label-free digital holotomography reveals ibuprofen-induced morphological changes to red blood cells. ACS Nanoscience Au, 3(3), 241-255. https://doi.org/10.1021/acsnanoscienceau.3c00004 |
| A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites
Bifulco, A., Casciello, A., Imparato, C., Forte, S., Gaan, S., Aronne, A., & Malucelli, G. (2023). A machine learning tool for future prediction of heat release capacity of in-situ flame retardant hybrid Mg(OH)2-Epoxy nanocomposites. Polymer Testing, 127, 108175 (8 pp.). https://doi.org/10.1016/j.polymertesting.2023.108175 |
| Facilitated machine learning for image-based fruit quality assessment
Knott, M., Perez-Cruz, F., & Defraeye, T. (2023). Facilitated machine learning for image-based fruit quality assessment. Journal of Food Engineering, 345, 111401 (9 pp.). https://doi.org/10.1016/j.jfoodeng.2022.111401 |
| Approximating optimal building retrofit solutions for large-scale retrofit analysis
Thrampoulidis, E., Hug, G., & Orehounig, K. (2023). Approximating optimal building retrofit solutions for large-scale retrofit analysis. Applied Energy, 333, 120566 (22 pp.). https://doi.org/10.1016/j.apenergy.2022.120566 |
| Machine learning of twin/matrix interfaces from local stress field
Troncoso, J. F., Hu, Y., della Ventura, N. M., Sharma, A., Maeder, X., & Turlo, V. (2023). Machine learning of twin/matrix interfaces from local stress field. Computational Materials Science, 228, 112322 (11 pp.). https://doi.org/10.1016/j.commatsci.2023.112322 |
| Unlocking the potential of CuAgZr metallic classes: a comprehensive exploration with combinatorial synthesis, high-throughput characterization, and machine learning
Wieczerzak, K., Groetsch, A., Pajor, K., Jain, M., Müller, A. M., Vockenhuber, C., … Michler, J. (2023). Unlocking the potential of CuAgZr metallic classes: a comprehensive exploration with combinatorial synthesis, high-throughput characterization, and machine learning. Advanced Science, 10(31), 2302997 (15 pp.). https://doi.org/10.1002/advs.202302997 |
| Regularised learning with selected physics for power system dynamics
Xie, H., Bellizio, F., Cremer, J. L., & Strbac, G. (2023). Regularised learning with selected physics for power system dynamics. In Power tech conference. 2023 IEEE Belgrade PowerTech (p. (7 pp.). https://doi.org/10.1109/PowerTech55446.2023.10202688 |
| GIR dataset: a geometry and real impulse response dataset for machine learning research in acoustics
Xydis, A., Perraudin, N., Rust, R., Heutschi, K., Casas, G., Grognuz, O. R., … Perez-Cruz, F. (2023). GIR dataset: a geometry and real impulse response dataset for machine learning research in acoustics. Applied Acoustics, 208, 109333 (12 pp.). https://doi.org/10.1016/j.apacoust.2023.109333 |
| Physically consistent neural ODEs for learning multi-physics systems
Zakwan, M., Di Natale, L., Svetozarevic, B., Heer, P., Jones, C. N., & Trecate Ferrari, G. (2023). Physically consistent neural ODEs for learning multi-physics systems. In H. Ishii, Y. Ebihara, Jichi Imura, & M. Yamakita (Eds.), IFAC PapersOnLine: Vol. 56. 22nd IFAC world congress, Yokohama, Japan, July 9-14, 2023 (pp. 5855-5860). https://doi.org/10.1016/j.ifacol.2023.10.079 |
| Machine learning-based screening for biomarkers of psoriasis and immune cell infiltration
Zhou, Y., Wang, Z., Han, L., Yu, Y., Guan, N., Fang, R., … Li, J. (2023). Machine learning-based screening for biomarkers of psoriasis and immune cell infiltration. European Journal of Dermatology, 33(2), 147-156. https://doi.org/10.1684/ejd.2023.4453 |
| A data-driven approach for window opening predictions in non-air-conditioned buildings
Fu, Y., Zhou, T., Lun, I., Khayatian, F., Deng, W., & Su, W. (2022). A data-driven approach for window opening predictions in non-air-conditioned buildings. Intelligent Buildings International, 14(3), 329-345. https://doi.org/10.1080/17508975.2021.1963651 |
| 3D printable soft sensory fiber networks for robust and complex tactile sensing
Hardman, D., George Thuruthel, T., Georgopoulou, A., Clemens, F., & Iida, F. (2022). 3D printable soft sensory fiber networks for robust and complex tactile sensing. Micromachines, 13(9), 1540 (16 pp.). https://doi.org/10.3390/mi13091540 |
| Reconstructing radial stem size changes of trees with machine learning
Luković, M., Zweifel, R., Thiry, G., Zhang, C., & Schubert, M. (2022). Reconstructing radial stem size changes of trees with machine learning. Journal of the Royal Society Interface, 19(194), 20220349 (13 pp.). https://doi.org/10.1098/rsif.2022.0349 |
| Higher-order accurate neural network for real-time fluid velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2022). Higher-order accurate neural network for real-time fluid velocimetry. In Proceedings of the 20th international symposium on the application of laser and imaging techniques to fluid mechanics 2022 (p. (13 pp.). Sine nomine. |
| Kinematic training of convolutional neural networks for particle image velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2022). Kinematic training of convolutional neural networks for particle image velocimetry. Measurement Science and Technology, 33(12), 124006 (16 pp.). https://doi.org/10.1088/1361-6501/ac8fae |
| Estimating BOS image deformation with a lightweight CNN
Mucignat, C., Manickathan, L., Shah, J., Rösgen, T., & Lunati, I. (2022). Estimating BOS image deformation with a lightweight CNN. In Proceedings of the 20th international symposium on the application of laser and imaging techniques to fluid mechanics 2022 (p. (14 pp.). Sine nomine. |
| Machine learning-based characterization of the nanostructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy film
Nagy, P., Kaszás, B., Csabai, I., Hegedűs, Z., Michler, J., Pethö, L., & Gubicza, J. (2022). Machine learning-based characterization of the nanostructure in a combinatorial Co-Cr-Fe-Ni compositionally complex alloy film. Nanomaterials, 12(24), 4407 (14 pp.). https://doi.org/10.3390/nano12244407 |
| Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs
Perera, A. T. D., Khayatian, F., Eggimann, S., Orehounig, K., & Halgamuge, S. (2022). Quantifying the climate and human-system-driven uncertainties in energy planning by using GANs. Applied Energy, 328, 120169 (12 pp.). https://doi.org/10.1016/j.apenergy.2022.120169 |