Valerii Iakovlev
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View article: Learning Spatiotemporal Dynamical Systems from Point Process Observations
Learning Spatiotemporal Dynamical Systems from Point Process Observations Open
Spatiotemporal dynamics models are fundamental for various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network-based spatiotemporal modeling approaches fall short when f…
View article: E(3)-equivariant models cannot learn chirality: Field-based molecular generation
E(3)-equivariant models cannot learn chirality: Field-based molecular generation Open
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, utilizing graph neural network (GNN) parametrizations, with rota…
View article: Learning Space-Time Continuous Neural PDEs from Partially Observed States
Learning Space-Time Continuous Neural PDEs from Partially Observed States Open
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 space-time continuous latent neural PDE model with an effici…
View article: Latent Neural ODEs with Sparse Bayesian Multiple Shooting
Latent Neural ODEs with Sparse Bayesian Multiple Shooting Open
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. These methods are often heuristics with poor th…
View article: Learning partial differential equations from data
Learning partial differential equations from data Open
Partial differential equations (PDEs) are ubiquitous in science and engineering for their ability to model the behavior of various systems. In science, PDEs are used to model a multitude of phenomena ranging from quantum mechanics to brain…
View article: Learning continuous-time PDEs from sparse data with graph neural networks
Learning continuous-time PDEs from sparse data with graph neural networks Open
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 PDEs directly from data, previous methods are limited to dis…
View article: Learning continuous-time PDEs from sparse data with graph neural\n networks
Learning continuous-time PDEs from sparse data with graph neural\n networks Open
The behavior of many dynamical systems follow complex, yet still unknown\npartial differential equations (PDEs). While several machine learning methods\nhave been proposed to learn PDEs directly from data, previous methods are\nlimited to …