2022
Babushkin, I., Galán, Á. J., de Andrade, J. R. C., Husakou, A., Morales, F., Kretschmar, M., Nagy, T., Vaičaitis, V., Shi, L., Zuber, D., Bergé, L., Skupin, S., Nikolaeva, I. A., Panov, N. A., Shipilo, D. E., Kosareva, O. G., Pfeiffer, A. N., Demircan, A., Vrakking, M. J. J., ... Ivanov, M. (2022). All-optical attoclock for imaging tunnelling wavepackets. Nature physics. doi.org/10.1038/s41567-022-01505-2
Babushkin, I., Demircan, A., Kues, M., & Morgner, U. (2022). Wave-Shape-Tolerant Photonic Quantum Gates. Physical Review Letters, 128(9), [090502]. doi.org/10.1103/PhysRevLett.128.090502
Brunotte, W., Nagel, L., Schneider, K., & Klünder, J. A-C. (2022). How to Identify Changing Contexts of Use with Creativity Workshops: An Experience Report. In C. Ardito, R. Lanzilotti, A. Malizia, M. Larusdottir, L. D. Spano, J. Campos, M. Hertzum, T. Mentler, J. Abdelnour Nocera, L. Piccolo, S. Sauer, & G. van der Veer (Eds.), Sense, Feel, Design: INTERACT 2021 IFIP TC 13 Workshops, Revised Selected Papers (pp. 88-97). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13198 LNCS). Springer Nature Switzerland AG. doi.org/10.1007/978-3-030-98388-8_9
Brunotte, W., Chazette, L., Klös, V., & Speith, T. (2022). Quo Vadis, Explainability? A Research Roadmap for Explainability Engineering. In V. Gervasi, & A. Vogelsang (Eds.), Requirements Engineering: Foundation for Software Quality : 28th International Working Conference, REFSQ 2022, Birmingham, UK, March 21–24, 2022, Proceedings (pp. 26-32). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13216 LNCS). Springer Nature Switzerland AG. doi.org/10.1007/978-3-030-98464-9_3
Guo, H., Zhuang, X., Chen, P., Alajlan, N., & Rabczuk, T. (2022). Analysis of three-dimensional potential problems in non-homogeneous media with physics-informed deep collocation method using material transfer learning and sensitivity analysis. Engineering with computers, 1-22. doi.org/10.1007/s00366-022-01633-6
Guo, HW., Zhuang, XY., Alajlan, N., & Rabczuk, T. (2022). Stochastic deep collocation method based on neural architecture search and transfer learning for heterogeneous porous media. Engineering with computers. doi.org/10.1007/s00366-021-01586-2
Hartig, M., Schuster, S., & Wanner, G. (Accepted/In press). Geometric tilt-to-length coupling in precision interferometry: mechanisms and analytical descriptions. Journal of Optics. doi.org/10.1088/2040-8986/ac675e
He, L., Guo, HW., Jin, Y., Zhuang, XY., Rabczuk, T., & Li, Y. (2022). Machine-learning-driven on-demand design of phononic beams. Science China: Physics, Mechanics and Astronomy, 65(1), [214612]. doi.org/10.1007/s11433-021-1787-x
Jin, Y., He, L., Wen, Z., Mortazavi, B., Guo, HW., Torrent, D., Djafari-Rouhani, B., Rabczuk, T., Zhuang, XY., & Li, Y. (2022). Intelligent on-demand design of phononic metamaterials. Nanophotonics, 11(3), 439-460. doi.org/10.1515/nanoph-2021-0639
Klepzig, L. F., Biesterfeld, L., Romain, M., Niebur, A., Schlosser, A., Hübner, J., & Lauth, J. (2022). Colloidal 2D PbSe nanoplatelets with efficient emission reaching the telecom O-, E- and S-band. Nanoscale Advances, 4(2), 590-599. doi.org/10.1039/d1na00704a
Liu, B., Vu-Bac, N., Fu, X., Zhuang, X., & Rabczuk, T. (2022). Stochastic full-range multiscale modeling of thermal conductivity of Polymeric carbon nanotubes composites: A machine learning approach. Composite structures, 289, [115393]. doi.org/10.1016/j.compstruct.2022.115393
Melchert, O., & Demircan, A. (2022). py-fmas: A python package for ultrashort optical pulse propagation in terms of forward models for the analytic signal. Computer Physics Communications, 273, [108257]. doi.org/10.1016/j.cpc.2021.108257
Mortazavi, B., Shojaei, F., Shapeev, A. V., & Zhuang, X. (2022). A combined first-principles and machine-learning investigation on the stability, electronic, optical, and mechanical properties of novel C6N7-based nanoporous carbon nitrides. CARBON, 194, 230-239. doi.org/10.1016/j.carbon.2022.03.068
Mortazavi, B., Shahrokhi, M., Shojaei, F., Rabczuk, T., Zhuang, X., & Shapeev, A. V. (2022). A first-principles and machine-learning investigation on the electronic, photocatalytic, mechanical and heat conduction properties of nanoporous C5N monolayers. NANOSCALE, 14(11), 4324-4333. doi.org/10.1039/d1nr06449e
Mortazavi, B., Rajabpour, A., Zhuang, XY., Rabczuk, T., & Shapeev, AV. (2022). Exploring thermal expansion of carbon-based nanosheets by machine-learning interatomic potentials. CARBON, 186, 501-508. doi.org/10.1016/j.carbon.2021.10.059
Mortazavi, B., Shahrokhi, M., Javvaji, B., Shapeev, A. V., & Zhuang, X. (2022). Highly anisotropic mechanical and optical properties of 2D NbOX2 (X= Cl, Br, I) revealed by first-principle. NANOTECHNOLOGY, 33(27), [275701]. doi.org/10.1088/1361-6528/ac622f
Mortazavi, B., Shahrokhi, M., Zhuang, XY., Rabczuk, T., & Shapeev, AV. (2022). Mechanical, thermal transport, electronic and photocatalytic properties of penta-PdPS, -PdPSe and -PdPTe monolayers explored by first-principles calculations. Journal of Materials Chemistry C, 10(1), 329-336. doi.org/10.1039/d1tc05297g
Mortazavi, B., Zhuang, X., Rabczuk, T., & Shapeev, A. V. (2022). Outstanding thermal conductivity and mechanical properties in the direct gap semiconducting penta-NiN2 monolayer confirmed by first-principles. Physica E: Low-Dimensional Systems and Nanostructures, 140, [115221]. doi.org/10.1016/j.physe.2022.115221
Roth, J., Schröder, M., & Wick, T. (2022). Neural network guided adjoint computations in dual weighted residual error estimation. SN Applied Sciences, 4(2), [62]. doi.org/10.1007/s42452-022-04938-9
Schlenkrich, J., Zámbó, D., Schlosser, A., Rusch, P., & Bigall, N. C. (2022). Revealing the Effect of Nanoscopic Design on the Charge Carrier Separation Processes in Semiconductor‐Metal Nanoparticle Gel Networks. Advanced optical materials, 10(1), [2101712]. doi.org/10.1002/adom.202101712
