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Found 4 papers, 2 papers with code
Convolution, aggregation and attention based deep neural networks for accelerating simulations in mechanics
We study and compare the performance of all three networks on two benchmark examples, and show their capabilities to accurately predict the non-linear mechanical responses of soft bodies.
MAgNET: A Graph U-Net Architecture for Mesh-Based Simulations
In this work, we present a novel encoder-decoder geometric deep learning framework called MAgNET, which extends the well-known convolutional neural networks to accommodate arbitrary graph-structured data.
Probabilistic Deep Learning for Real-Time Large Deformation Simulations
For many novel applications, such as patient-specific computer-aided surgery, conventional solution techniques of the underlying nonlinear problems are usually computationally too expensive and are lacking information about how certain can we be about their predictions.
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Can one build a knowledge graph (KG) for all products in the world?
