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Found 5 papers, 3 papers with code
Field-based Molecule Generation
This work introduces FMG, a field-based model for drug-like molecule generation.
Learning Space-Time Continuous Neural PDEs from Partially Observed States
We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids.
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Training dynamic models, such as neural ODEs, on long trajectories is a hard problem that requires using various tricks, such as trajectory splitting, to make model training work in practice.
Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids
Data-driven neural network models have recently shown great success in modelling and learning complex PDE systems.
Learning continuous-time PDEs from sparse data with graph neural networks
We demonstrate the model's ability to work with unstructured grids, arbitrary time steps, and noisy observations.
