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 20 articles )

Showing results 1 to 20 of 20.
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  1. Keller, Rachael T.; Du, Qiang: Discovery of dynamics using linear multistep methods (2021)
  2. Matsuda, Takeru; Miyatake, Yuto: Generalization of partitioned Runge-Kutta methods for adjoint systems (2021)
  3. Su, Wei-Hung; Chou, Ching-Shan; Xiu, Dongbin: Deep learning of biological models from data: applications to ODE models (2021)
  4. Brehmer, Johann; Louppe, Gilles; Pavez, Juan; Cranmer, Kyle: Mining gold from implicit models to improve likelihood-free inference (2020)
  5. Chen, Zhen; Wu, Kailiang; Xiu, Dongbin: Methods to recover unknown processes in partial differential equations using data (2020)
  6. Drori, Iddo: Deep variational inference (2020)
  7. Kazemi, Seyed Mehran; Goel, Rishab; Jain, Kshitij; Kobyzev, Ivan; Sethi, Akshay; Forsyth, Peter; Poupart, Pascal: Representation learning for dynamic graphs: a survey (2020)
  8. Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park: TorchDyn: A Neural Differential Equations Library (2020) arXiv
  9. 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)
  10. Ruthotto, Lars; Haber, Eldad: Deep neural networks motivated by partial differential equations (2020)
  11. Zhang, Hai-Miao; Dong, Bin: A review on deep learning in medical image reconstruction (2020)
  12. Zou, Yunlei; Qian, Chunjiang; He, Shuaipeng: A necessary and sufficient condition for stability of a class of planar nonlinear systems (2020)
  13. Benning, Martin; Celledoni, Elena; Ehrhardt, Matthias J.; Owren, Brynjulf; Schönlieb, Carola-Bibiane: Deep learning as optimal control problems: models and numerical methods (2019)
  14. E, Weinan; Han, Jiequn; Li, Qianxiao: A mean-field optimal control formulation of deep learning (2019)
  15. Long, Zichao; Lu, Yiping; Dong, Bin: PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network (2019)
  16. Qin, Tong; Wu, Kailiang; Xiu, Dongbin: Data driven governing equations approximation using deep neural networks (2019)
  17. Rudy, Samuel H.; Nathan Kutz, J.; Brunton, Steven L.: Deep learning of dynamics and signal-noise decomposition with time-stepping constraints (2019)
  18. San, Omer; Maulik, Romit; Ahmed, Mansoor: An artificial neural network framework for reduced order modeling of transient flows (2019)
  19. Stinis, Panos; Hagge, Tobias; Tartakovsky, Alexandre M.; Yeung, Enoch: Enforcing constraints for interpolation and extrapolation in generative adversarial networks (2019)
  20. Tronarp, Filip; Kersting, Hans; Särkkä, Simo; Hennig, Philipp: Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective (2019)