Profil

SOSA Raul Ian

University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Physics and Materials Science (DPHYMS)

Main Referenced Co-authors
BORDAS, Stéphane  (3)
LENGIEWICZ, Jakub  (3)
DESHPANDE, Saurabh  (2)
SHEN, Zhaoxiang  (1)
TKATCHENKO, Alexandre  (1)
Main Referenced Keywords
Carbon nanotube (1); CNN U-NET (1); Deep Learning (1); Deep learning (1); Density-functional tight-binding (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (1)
Main Referenced Disciplines
Engineering, computing & technology: Multidisciplinary, general & others (3)

Publications (total 3)

The most downloaded
60 downloads
DESHPANDE, S., SOSA, R. I., BORDAS, S., & LENGIEWICZ, J. (2023). Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics. Frontiers in Materials. doi:10.3389/fmats.2023.1128954 https://hdl.handle.net/10993/52915

The most cited

15 citations (OpenAlex)

DESHPANDE, S., SOSA, R. I., BORDAS, S., & LENGIEWICZ, J. (2023). Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics. Frontiers in Materials. doi:10.3389/fmats.2023.1128954 https://hdl.handle.net/10993/52915

SHEN, Z., SOSA, R. I., BORDAS, S., TKATCHENKO, A., & LENGIEWICZ, J. (01 November 2024). Quantum-informed simulations for mechanics of materials: DFTB+MBD framework. International Journal of Engineering Science, 204, 104126. doi:10.1016/j.ijengsci.2024.104126
Peer Reviewed verified by ORBi

DESHPANDE, S., SOSA, R. I., BORDAS, S., & LENGIEWICZ, J. (August 2023). Novel deep learning approaches for learning scientific simulations [Paper presentation]. The 14th International Conference of Computational Methods (ICCM2023), Ho Chi Minh, Vietnam.
Peer reviewed

DESHPANDE, S., SOSA, R. I., BORDAS, S., & LENGIEWICZ, J. (2023). Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics. Frontiers in Materials. doi:10.3389/fmats.2023.1128954
Peer Reviewed verified by ORBi

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