Recent advances in reinforcement learning (RL) have increased the promise of introducing
cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS) …

Abstract The Finite Element Method, an important technique in engineering, is aided by
Adaptive Mesh Refinement (AMR), which dynamically refines mesh regions to allow for a …

Recently, many mesh-based graph neural network (GNN) models have been proposed for
modeling complex high-dimensional physical systems. Remarkable achievements have …

Accurately simulating real world object dynamics is essential for various applications such
as robotics, engineering, graphics, and design. To better capture complex real dynamics …

Simulating physical systems is essential in engineering, but analytical solutions are limited
to straightforward problems. Consequently, numerical methods like the Finite Element …

Tensegrity robots are composed of rigid struts and flexible cables. They constitute an
emerging class of hybrid rigid-soft robotic systems and are promising systems for a wide …

Learning-based simulators show great potential for simulating particle dynamics when 3D
groundtruth is available, but per-particle correspondences are not always accessible. The …

In recent years, Graph Neural Network (GNN) based models have shown promising results
in simulating physics of complex systems. However, training dedicated graph network based …

In graph learning the graph and the node features both contain noisy information about the
node labels. In this paper we propose joint denoising and rewiring (JDR)--an algorithm to …

Modeling the structure and events of the physical world constitutes a fundamental objective
of neural networks. Among the diverse approaches, Graph Network Simulators (GNS) have …