8 citations · 12 across the 5 of their papers we have counts for
5 papers
Learning Spatiotemporal Dynamical Systems from Point Process Observations
Valerii Iakovlev, Harri Lähdesmäki
Spatiotemporal dynamics models are fundamental for various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…
Learning Space-Time Continuous Neural PDEs from Partially Observed States
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a s…
Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Valerii Iakovlev, Cagatay Yildiz, Markus Heinonen +1
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 i…
Learning continuous-time PDEs from sparse data with graph neural networks
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn P…