77 citations · 188 across the 4 of their papers we have counts for
4 papers
Gaussian process regression + deep neural network autoencoder for probabilistic surrogate modeling in nonlinear mechanics of solids
Saurabh Deshpande, Hussein Rappel, Mark Hobbs +2
Many real-world applications demand accurate and fast predictions, as well as reliable uncertainty estimates. However, quantifying uncertainty on high-dimensional predictions is st…
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
Saurabh Deshpande, Raúl I. Sosa, Stéphane P. A. Bordas +1
Deep learning surrogate models are being increasingly used in accelerating scientific simulations as a replacement for costly conventional numerical techniques. However, their use…
MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations
Saurabh Deshpande, Stéphane P. A. Bordas, Jakub Lengiewicz
In many cutting-edge applications, high-fidelity computational models prove to be too slow for practical use and are therefore replaced by much faster surrogate models. Recently, d…
Probabilistic Deep Learning for Real-Time Large Deformation Simulations
Saurabh Deshpande, Jakub Lengiewicz, Stéphane P. A. Bordas
For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too…