77 citations · 327 across the 6 of their papers we have counts for
5 papers
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…
Finite deformations govern the anisotropic shear-induced area reduction of soft elastic contacts
J. Lengiewicz, M. de Souza, M. Lahmar +4
Solid contacts involving soft materials are important in mechanical engineering or biomechanics. Experimentally, such contacts have been shown to shrink significantly under shear,…
Distributed prediction of unsafe reconfiguration scenarios of modular robotic Programmable Matter
Benoît Piranda, Paweł Chodkiewicz, Paweł Hołobut +3
We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose st…