Robot Model Identification and Learning: A Modern Perspective
In recent years, the increasing complexity and safety-critical nature of robotic tasks have
highlighted the importance of accurate and reliable robot models. This trend has led to a …
highlighted the importance of accurate and reliable robot models. This trend has led to a …
Towards understanding generalization of graph neural networks
H Tang, Y Liu - International Conference on Machine …, 2023 - proceedings.mlr.press
Graph neural networks (GNNs) are widely used in machine learning for graph-structured
data. Even though GNNs have achieved remarkable success in real-world applications …
data. Even though GNNs have achieved remarkable success in real-world applications …
Physics-aware machine learning revolutionizes scientific paradigm for machine learning and process-based hydrology
Accurate hydrological understanding and water cycle prediction are crucial for addressing
scientific and societal challenges associated with the management of water resources …
scientific and societal challenges associated with the management of water resources …
Equivariant spatio-temporal attentive graph networks to simulate physical dynamics
Learning to represent and simulate the dynamics of physical systems is a crucial yet
challenging task. Existing equivariant Graph Neural Network (GNN) based methods have …
challenging task. Existing equivariant Graph Neural Network (GNN) based methods have …
Latent field discovery in interacting dynamical systems with neural fields
Abstract Systems of interacting objects often evolve under the influence of underlying field
effects that govern their dynamics, yet previous works have abstracted away from such …
effects that govern their dynamics, yet previous works have abstracted away from such …
Unifying predictions of deterministic and stochastic physics in mesh-reduced space with sequential flow generative model
Accurate prediction of dynamical systems in unstructured meshes has recently shown
successes in scientific simulations. Many dynamical systems have a nonnegligible level of …
successes in scientific simulations. Many dynamical systems have a nonnegligible level of …
Geometry-complete perceptron networks for 3d molecular graphs
Motivation The field of geometric deep learning has recently had a profound impact on
several scientific domains such as protein structure prediction and design, leading to …
several scientific domains such as protein structure prediction and design, leading to …
Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems
We develop inductive biases for the machine learning of complex physical systems based
on the port-Hamiltonian formalism. To satisfy by construction the principles of …
on the port-Hamiltonian formalism. To satisfy by construction the principles of …
Equivariant graph neural operator for modeling 3d dynamics
Modeling the complex three-dimensional (3D) dynamics of relational systems is an
important problem in the natural sciences, with applications ranging from molecular …
important problem in the natural sciences, with applications ranging from molecular …
Subequivariant graph reinforcement learning in 3D environments
Learning a shared policy that guides the locomotion of different agents is of core interest in
Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL …
Reinforcement Learning (RL), which leads to the study of morphology-agnostic RL …