Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Iakovlev, Valerii"'
Autor:
Iakovlev, Valerii, Lähdesmäki, Harri
Spatiotemporal processes are a fundamental tool for modeling dynamics across various domains, from heat propagation in materials to oceanic and atmospheric flows. However, currently available neural network-based modeling approaches fall short when f
Externí odkaz:
http://arxiv.org/abs/2406.00368
Autor:
Dumitrescu, Alexandru, Korpela, Dani, Heinonen, Markus, Verma, Yogesh, Iakovlev, Valerii, Garg, Vikas, Lähdesmäki, Harri
This work introduces FMG, a field-based model for drug-like molecule generation. We show how the flexibility of this method provides crucial advantages over the prevalent, point-cloud based methods, and achieves competitive molecular stability genera
Externí odkaz:
http://arxiv.org/abs/2402.15864
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 efficient probabi
Externí odkaz:
http://arxiv.org/abs/2307.04110
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 theoretical j
Externí odkaz:
http://arxiv.org/abs/2210.03466
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 discrete-time
Externí odkaz:
http://arxiv.org/abs/2006.08956