Search Results for author:
Found 5 papers, 1 papers with code
Discovering Message Passing Hierarchies for Mesh-Based Physics Simulation
Graph neural networks have emerged as a powerful tool for large-scale mesh-based physics simulation.
Latent Intuitive Physics: Learning to Transfer Hidden Physics from A 3D Video
We introduce latent intuitive physics, a transfer learning framework for physics simulation that can infer hidden properties of fluids from a single 3D video and simulate the observed fluid in novel scenes.
LayoutFormer++: Conditional Graphic Layout Generation via Constraint Serialization and Decoding Space Restriction
First, to flexibly handle diverse constraints, we propose a constraint serialization scheme, which represents different user constraints as sequences of tokens with a predefined format.
NeuroFluid: Fluid Dynamics Grounding with Particle-Driven Neural Radiance Fields
Deep learning has shown great potential for modeling the physical dynamics of complex particle systems such as fluids.
Adversarial Invariant Learning
To the best of our knowledge, this is one of the first to adopt differentiable environment splitting method to enable stable predictions across environments without environment index information, which achieves the state-of-the-art performance on datasets with strong spurious correlation, such as Colored MNIST.
