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 59 articles )

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  1. E, Weinan; Han, Jiequn; Jentzen, Arnulf: Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (2022)
  2. Hofmann, S.; Borzì, A.: A sequential quadratic Hamiltonian algorithm for training explicit RK neural networks (2022)
  3. Ren, Pu; Rao, Chengping; Liu, Yang; Wang, Jian-Xun; Sun, Hao: PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (2022)
  4. Yang, Liu; Daskalakis, Constantinos; Karniadakis, George E.: Generative ensemble regression: Learning particle dynamics from observations of ensembles with physics-informed deep generative models (2022)
  5. Yoon, Ryeongkyung; Bhat, Harish S.; Osting, Braxton: A nonautonomous equation discovery method for time signal classification (2022)
  6. Avelin, Benny; Nyström, Kaj: Neural ODEs as the deep limit of ResNets with constant weights (2021)
  7. Bilal, Anas; Sun, Guangmin; Mazhar, Sarah; Junjie, Zhang: Neuro-optimized numerical treatment of HIV infection model (2021)
  8. Bungert, Leon; Hait-Fraenkel, Ester; Papadakis, Nicolas; Gilboa, Guy: Nonlinear power method for computing eigenvectors of proximal operators and neural networks (2021)
  9. Celledoni, E.; Ehrhardt, M. J.; Etmann, C.; McLachlan, R. I.; Owren, B.; Schonlieb, C.-B.; Sherry, F.: Structure-preserving deep learning (2021)
  10. Chatigny, Philippe; Patenaude, Jean-Marc; Wang, Shengrui: Spatiotemporal adaptive neural network for long-term forecasting of financial time series (2021)
  11. Chen, Xiaoli; Duan, Jinqiao; Karniadakis, George Em: Learning and meta-learning of stochastic advection-diffusion-reaction systems from sparse measurements (2021)
  12. Colas, Cédric; Hejblum, Boris; Rouillon, Sebastien; Thiébaut, Rodolphe; Oudeyer, Pierre-Yves; Moulin-Frier, Clément; Prague, Mélanie: EpidemiOptim: a toolbox for the optimization of control policies in epidemiological models (2021)
  13. El Ghaoui, Laurent; Gu, Fangda; Travacca, Bertrand; Askari, Armin; Tsai, Alicia: Implicit deep learning (2021)
  14. Fan, Jianqing; Ma, Cong; Zhong, Yiqiao: A selective overview of deep learning (2021)
  15. Forgione, Marco; Piga, Dario: Continuous-time system identification with neural networks: model structures and fitting criteria (2021)
  16. Giesecke, Elisa; Kröner, Axel: Classification with Runge-Kutta networks and feature space augmentation (2021)
  17. Gusak, J.; Daulbaev, T.; Ponomarev, E.; Cichocki, A.; Oseledets, I.: Reduced-order modeling of deep neural networks (2021)
  18. Hagemann, Paul; Neumayer, Sebastian: Stabilizing invertible neural networks using mixture models (2021)
  19. Hammadi, Youssef; Ryckelynck, David; El-Bakkali, Amin: Data-driven reduced bond graph for nonlinear multiphysics dynamic systems (2021)
  20. Ito, Shin-ichi; Matsuda, Takeru; Miyatake, Yuto: Adjoint-based exact Hessian computation (2021)

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