| Learning continuous-time pdes from sparse data with graph neural networks V Iakovlev, M Heinonen, H Lähdesmäki arXiv preprint arXiv:2006.08956, 2020 | 71 | 2020 |
| Latent neural ODEs with sparse bayesian multiple shooting V Iakovlev, C Yildiz, M Heinonen, H Lähdesmäki arXiv preprint arXiv:2210.03466, 2022 | 12 | 2022 |
| Modeling Randomly Observed Spatiotemporal Dynamical Systems V Iakovlev, H Lähdesmäki arXiv preprint arXiv:2406.00368, 2024 | | 2024 |
| Field-based Molecule Generation A Dumitrescu, D Korpela, M Heinonen, Y Verma, V Iakovlev, V Garg, ... arXiv preprint arXiv:2402.15864, 2024 | | 2024 |
| Learning Space-Time Continuous Latent Neural PDEs from Partially Observed States V Iakovlev, M Heinonen, H Lähdesmäki Advances in Neural Information Processing Systems 36, 2024 | | 2024 |
| Learning space-time continuous neural PDEs from partially observed states V Iakovlev, M Heinonen, H Lähdesmäki arXiv preprint arXiv:2307.04110, 2023 | | 2023 |
| Learning partial differential equations from data V Iakovlev | | 2020 |
| Enforcing physics-based algebraic constraints for inference of PDE models on unstructured grids V Iakovlev, M Heinonen, H Lähdesmäki | | |
