Back
  1. Home
  2. Profile of Saurabh DESHPANDE (Unilu)
Profil

DESHPANDE Saurabh

University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)

ORCID
0000-0002-1813-9735
Main Referenced Co-authors
BORDAS, Stéphane  (12)
LENGIEWICZ, Jakub  (9)
MAZIER, Arnaud  (2)
SOSA, Raul Ian  (2)
BEEX, Lars  (1)
Main Referenced Keywords
Deep Learning (2); Finite Element Method (2); Surrogate Modeling (2); Autoencoders (1); Bayesian Deep learning (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (3)
Main Referenced Disciplines
Engineering, computing & technology: Multidisciplinary, general & others (10)
Computer science (2)
Mechanical engineering (1)

Publications (total 13)

The most downloaded
289 downloads
DESHPANDE, S., LENGIEWICZ, J., & BORDAS, S. (01 August 2022). Probabilistic Deep Learning for Real-Time Large Deformation Simulations. Computer Methods in Applied Mechanics and Engineering, 398 (0045-7825), 115307. doi:10.1016/j.cma.2022.115307 https://hdl.handle.net/10993/51869

The most cited

34 citations (Scopus®)

DESHPANDE, S., LENGIEWICZ, J., & BORDAS, S. (01 August 2022). Probabilistic Deep Learning for Real-Time Large Deformation Simulations. Computer Methods in Applied Mechanics and Engineering, 398 (0045-7825), 115307. doi:10.1016/j.cma.2022.115307 https://hdl.handle.net/10993/51869

DESHPANDE, S., RAPPEL, H., Hobbs, M., BORDAS, S., & Lengiewicz, J. (2024). Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/61242.

DESHPANDE, S. (2023). Data Driven Surrogate Frameworks for Computational Mechanics: Bayesian and Geometric Deep Learning Approaches [Doctoral thesis, Unilu - University of Luxembourg]. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/57321

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., LENGIEWICZ, J., & BORDAS, S. (27 June 2023). Novel Geometric Deep Learning Surrogate Framework for Non-Linear Finite Element Simulations [Poster presentation]. The Platform for Advanced Scientific Computing (PASC) Conference 2023.
Peer reviewed

DESHPANDE, S., BORDAS, S., & LENGIEWICZ, J. (2023). MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/54969.

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

DESHPANDE, S., LENGIEWICZ, J., & BORDAS, S. (01 August 2022). Probabilistic Deep Learning for Real-Time Large Deformation Simulations. Computer Methods in Applied Mechanics and Engineering, 398 (0045-7825), 115307. doi:10.1016/j.cma.2022.115307
Peer Reviewed verified by ORBi

DESHPANDE, S., LENGIEWICZ, J., & BORDAS, S. (2022). Real Time Hyper-elastic Simulations with Probabilistic Deep Learning. In 15th World Congress on Computational Mechanics (WCCM-XV).
Peer reviewed

MAZIER, A., LAVIGNE, T., LENGIEWICZ, J., DESHPANDE, S., URCUN, S., & BORDAS, S. (July 2022). Towards real-time patient-specific breast simulations: from full-field information to surrogate model [Paper presentation]. 9th World Congress of Biomechanics.

DESHPANDE, S., LENGIEWICZ, J., & BORDAS, S. (28 June 2022). Real-Time Large Deformation Simulations Using Probabilistic Deep Learning Framework [Poster presentation]. The Platform for Advanced Scientific Computing (PASC) Conference.

DESHPANDE, S., LENGIEWICZ, J., & BORDAS, S. (2022). Real-time large deformations: A probabilistic deep learning approach. In The 8th European Congress on Computational Methods in Applied Sciences and Engineering.
Peer reviewed

DESHPANDE, S., BORDAS, S., BEEX, L., Cotin, S., & Sarkica, A. (July 2020). DATA DRIVEN SURGICAL SIMULATIONS [Paper presentation]. 14th World Congress on Computational Mechanics (WCCM), Paris, France.

MAZIER, A., DESHPANDE, S., & BORDAS, S. (November 2019). DIGITAL TWINNING FOR REAL-TIME SIMULATION [Poster presentation]. EIB Annual Economics Conference Tech Fair.

Contact ORBilu