torchdiffeq

torchdiffeq: PyTorch Implementation of Differentiable ODE Solvers. This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through all solvers is supported using the adjoint method. For usage of ODE solvers in deep learning applications, see [1]. As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.


References in zbMATH (referenced in 31 articles )

Showing results 1 to 20 of 31.
Sorted by year (citations)

1 2 next

  1. Avelin, Benny; Nyström, Kaj: Neural ODEs as the deep limit of ResNets with constant weights (2021)
  2. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  3. Forgione, Marco; Piga, Dario: Continuous-time system identification with neural networks: model structures and fitting criteria (2021)
  4. Gusak, J.; Daulbaev, T.; Ponomarev, E.; Cichocki, A.; Oseledets, I.: Reduced-order modeling of deep neural networks (2021)
  5. Hagemann, Paul; Neumayer, Sebastian: Stabilizing invertible neural networks using mixture models (2021)
  6. Ito, Shin-ichi; Matsuda, Takeru; Miyatake, Yuto: Adjoint-based exact Hessian computation (2021)
  7. Keller, Rachael T.; Du, Qiang: Discovery of dynamics using linear multistep methods (2021)
  8. Lim, Soon Hoe: Understanding recurrent neural networks using nonequilibrium response theory (2021)
  9. Matsuda, Takeru; Miyatake, Yuto: Generalization of partitioned Runge-Kutta methods for adjoint systems (2021)
  10. Papamakarios, George; Nalisnick, Eric; Rezende, Danilo Jimenez; Mohamed, Shakir; Lakshminarayanan, Balaji: Normalizing flows for probabilistic modeling and inference (2021)
  11. Su, Wei-Hung; Chou, Ching-Shan; Xiu, Dongbin: Deep learning of biological models from data: applications to ODE models (2021)
  12. Brehmer, Johann; Louppe, Gilles; Pavez, Juan; Cranmer, Kyle: Mining gold from implicit models to improve likelihood-free inference (2020)
  13. Chen, Zhen; Wu, Kailiang; Xiu, Dongbin: Methods to recover unknown processes in partial differential equations using data (2020)
  14. Drori, Iddo: Deep variational inference (2020)
  15. E, Weinan; Ma, Chao; Wu, Lei: Machine learning from a continuous viewpoint. I (2020)
  16. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  17. Lorin, E.: Derivation and analysis of parallel-in-time neural ordinary differential equations (2020)
  18. Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park: TorchDyn: A Neural Differential Equations Library (2020) arXiv
  19. Ouala, S.; Nguyen, D.; Drumetz, L.; Chapron, B.; Pascual, A.; Collard, F.; Gaultier, L.; Fablet, R.: Learning latent dynamics for partially observed chaotic systems (2020)
  20. Ruthotto, Lars; Haber, Eldad: Deep neural networks motivated by partial differential equations (2020)

1 2 next