Active Filters

  • (-) … = empa-units:18
Search Results 1 - 20 of 601

Pages

  • RSS Feed
Select Page
Robust symbolic regression for network trajectory inference
Dakhmouche, R., Lunati, I., & Gorji, H. (2024). Robust symbolic regression for network trajectory inference. In ICLR 2024 workshop on machine learning for genomics explorations (pp. 1-17).
Exponential BGK integrator for multiscale flow simulation
Garmirian, F., Gorji, H., & Pfeiffer, M. (2024). Exponential BGK integrator for multiscale flow simulation. In R. S. Myong, K. Xu, & J. S. Wu (Eds.), AIP conference proceedings: Vol. 2996. International symposium on rarefied gas dynamics (p. 060005). https://doi.org/10.1063/5.0187429
A soft robotic morphing wing for unmanned underwater vehicles
Giordano, A., Achenbach, L., Lenggenhager, D., Wiesemüller, F., Vonbank, R., Mucignat, C., … Kovač, M. (2024). A soft robotic morphing wing for unmanned underwater vehicles. Advanced Intelligent Systems. https://doi.org/10.1002/aisy.202300702
Wasserstein-penalized Entropy closure: a use case for stochastic particle methods
Sadr, M., Hadjiconstantinou, N. G., & Gorji, M. H. (2024). Wasserstein-penalized Entropy closure: a use case for stochastic particle methods. Journal of Computational Physics, 511, 113066 (27 pp.). https://doi.org/10.1016/j.jcp.2024.113066
Learning control for body caudal undulation with soft sensory feedback
Schwab, F., El Arayshi, M., Rezaei, S., Sprumont, H., Allione, F., Mucignat, C., … Jusufi, A. (2024). Learning control for body caudal undulation with soft sensory feedback. Frontiers in Sensors, 5, 1367992 (13 pp.). https://doi.org/10.3389/fsens.2024.1367992
Simultaneous PIV–LIF measurements using RuPhen and a color camera
Shah, J., Mucignat, C., Lunati, I., & Rösgen, T. (2024). Simultaneous PIV–LIF measurements using RuPhen and a color camera. Experiments in Fluids, 65, 3 (15 pp.). https://doi.org/10.1007/s00348-023-03742-4
Asymmetric fin shape changes swimming dynamics of ancient marine reptiles' soft robophysical models
Sprumont, H., Allione, F., Schwab, F., Wang, B., Mucignat, C., Lunati, I., … Jusufi, A. (2024). Asymmetric fin shape changes swimming dynamics of ancient marine reptiles' soft robophysical models. Bioinspiration and Biomimetics, 19(4), 046005 (10 pp.). https://doi.org/10.1088/1748-3190/ad3f5e
Amorphous matters: heterogeneity and defects of nanopore silica surfaces enhance CO<sub>2</sub> adsorption
Turchi, M., Galmarini, S., & Lunati, I. (2024). Amorphous matters: heterogeneity and defects of nanopore silica surfaces enhance CO2 adsorption. Journal of Non-Crystalline Solids, 624, 122709 (11 pp.). https://doi.org/10.1016/j.jnoncrysol.2023.122709
A machine learning approach for computation of cardiovascular intrinsic frequencies
Alavi, R., Wang, Q., Gorji, H., & Pahlevan, N. M. (2023). A machine learning approach for computation of cardiovascular intrinsic frequencies. PLoS One, 18(10), e0285228 (20 pp.). https://doi.org/10.1371/journal.pone.0285228
Data-driven stochastic particle scheme for collisional plasma simulations
Chung, K., Fei, F., Gorji, M. H., & Jenny, P. (2023). Data-driven stochastic particle scheme for collisional plasma simulations. Journal of Computational Physics, 492, 112400 (15 pp.). https://doi.org/10.1016/j.jcp.2023.112400
Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January-March 2022)
Gorji, H., Stauffer, N., Lunati, I., Caduff, A., Bühler, M., Engel, D., … Renz, H. (2023). Projection of healthcare demand in Germany and Switzerland urged by Omicron wave (January-March 2022). Epidemics, 43, 100680 (9 pp.). https://doi.org/10.1016/j.epidem.2023.100680
Superhydrophobic and flexible aerogels and xerogels derived from organosilane precursors
Kanamori, K., Stojanovic, A., Pajonk, G. M., Nadargi, D. Y., Rao, A. V., Nakanishi, K., & Koebel, M. M. (2023). Superhydrophobic and flexible aerogels and xerogels derived from organosilane precursors. In M. A. Aegerter, N. Leventis, M. Koebel, & S. A. Steiner III (Eds.), Springer handbooks. Springer handbook of aerogels (pp. 367-391). https://doi.org/10.1007/978-3-030-27322-4_15
Critical assessment of various particle Fokker-Planck models for monatomic rarefied gas flows
Kim, S., Gorji, H., & Jun, E. (2023). Critical assessment of various particle Fokker-Planck models for monatomic rarefied gas flows. Physics of Fluids, 35(4), 046117 (23 pp.). https://doi.org/10.1063/5.0143195
A lightweight neural network designed for fluid velocimetry
Manickathan, L., Mucignat, C., & Lunati, I. (2023). A lightweight neural network designed for fluid velocimetry. Experiments in Fluids, 64(10), 161 (17 pp.). https://doi.org/10.1007/s00348-023-03695-8
Prediction of the surface chemistry of calcium aluminosilicate glasses
Miri Ramsheh, S., Turchi, M., Perera, S., Schade, A. M., Okhrimenko, D. V., Stipp, S. L. S., … Andersson, M. P. (2023). Prediction of the surface chemistry of calcium aluminosilicate glasses. Journal of Non-Crystalline Solids, 620, 122597 (7 pp.). https://doi.org/10.1016/j.jnoncrysol.2023.122597
A lightweight convolutional neural network to reconstruct deformation in BOS recordings
Mucignat, C., Manickathan, L., Shah, J., Rösgen, T., & Lunati, I. (2023). A lightweight convolutional neural network to reconstruct deformation in BOS recordings. Experiments in Fluids, 64(4), 72 (16 pp.). https://doi.org/10.1007/s00348-023-03618-7
Atomistic simulations of calcium aluminosilicate interfaced with liquid water
Vuković, F., Garcia, N. A., Perera, S., Turchi, M., Andersson, M. P., Solvang, M., … Walsh, T. R. (2023). Atomistic simulations of calcium aluminosilicate interfaced with liquid water. Journal of Chemical Physics, 159(10), 104704 (11 pp.). https://doi.org/10.1063/5.0164817
Wicking dynamics in yarns
Fischer, R., Schlepütz, C. M., Zhao, J., Boillat, P., Hegemann, D., Rossi, R. M., … Carmeliet, J. (2022). Wicking dynamics in yarns. Journal of Colloid and Interface Science, 625, 1-11. https://doi.org/10.1016/j.jcis.2022.04.060
Wicking fingering in electrospun membranes
Fischer, R., Schoeller, J., Rossi, R. M., Derome, D., & Carmeliet, J. (2022). Wicking fingering in electrospun membranes. Soft Matter, 18(30), 5662-5675. https://doi.org/10.1039/d2sm00472k
Wicking through complex interfaces at interlacing yarns
Fischer, R., Schlepütz, C. M., Rossi, R. M., Derome, D., & Carmeliet, J. (2022). Wicking through complex interfaces at interlacing yarns. Journal of Colloid and Interface Science, 626, 416-425. https://doi.org/10.1016/j.jcis.2022.06.103
 

Pages