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Found 8 papers, 4 papers with code
Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes
This work introduces PI-MGNs, a hybrid approach that combines PINNs and MGNs to quickly and accurately solve non-stationary and nonlinear partial differential equations (PDEs) on arbitrary meshes.
Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects
Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions.
Latent Task-Specific Graph Network Simulators
Movement primitives further allow us to accommodate various types of context data, as demonstrated through the utilization of point clouds during inference.
Swarm Reinforcement Learning For Adaptive Mesh Refinement
Adaptive Mesh Refinement (AMR) enhances the Finite Element Method, an important technique for simulating complex problems in engineering, by dynamically refining mesh regions, enabling a favorable trade-off between computational speed and simulation accuracy.
Grounding Graph Network Simulators using Physical Sensor Observations
Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.
Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
We find that the geometric descriptors greatly help in generalizing to new task configurations and that combining them with our distribution-matching objective is crucial for representing and reproducing versatile behavior.
Versatile Inverse Reinforcement Learning via Cumulative Rewards
Inverse Reinforcement Learning infers a reward function from expert demonstrations, aiming to encode the behavior and intentions of the expert.
Bimodal Speech Emotion Recognition Using Pre-Trained Language Models
Speech emotion recognition is a challenging task and an important step towards more natural human-machine interaction.
