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Found 7 papers, 0 papers with code
Simulation of hyperelastic materials in real-time using Deep Learning
In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm.
Elastic registration based on compliance analysis and biomechanical graph matching
ACGM is better than the previous Biomechanical Graph Matching method 3 (BGM) because it uses an efficient biomechanical vascularized liver model to compute the organ's transformation and the vessels bifurcations compliance.
DeepPhysics: a physics aware deep learning framework for real-time simulation
In this paper, we propose a solution to simulate hyper-elastic materials using a data-driven approach, where a neural network is trained to learn the non-linear relationship between boundary conditions and the resulting displacement field.
CNN-based real-time 2D-3D deformable registration from a single X-ray projection
From this dataset, a neural network is trained to recover the unknown 3D displacement field from a single projection image.
Real-time elastic partial shape matching using a neural network-based adjoint method
Surface matching usually provides significant deformations that can lead to structural failure due to the lack of physical policy.
Deformable Image Registration with Stochastically Regularized Biomechanical Equilibrium
Numerous regularization methods for deformable image registration aim at enforcing smooth transformations, but are difficult to tune-in a priori and lack a clear physical basis.
A Zero-Shot Reinforcement Learning Strategy for Autonomous Guidewire Navigation
Deep Reinforcement Learning approaches have shown promise in learning this task and may be the key to automating catheter navigation during robotized interventions.
