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. Keller, Rachael T.; Du, Qiang: Discovery of dynamics using linear multistep methods (2021)
  2. Li, Jingshi; Chen, Song; Cao, Yanzhao; Sun, Zhao: A neural network approach to sampling based learning control for quantum system with uncertainty (2021)
  3. Lim, Soon Hoe: Understanding recurrent neural networks using nonequilibrium response theory (2021)
  4. Matsuda, Takeru; Miyatake, Yuto: Generalization of partitioned Runge-Kutta methods for adjoint systems (2021)
  5. Newman, Elizabeth; Ruthotto, Lars; Hart, Joseph; van Bloemen Waanders, Bart: Train like a (Var)pro: efficient training of neural networks with variable projection (2021)
  6. Papamakarios, George; Nalisnick, Eric; Rezende, Danilo Jimenez; Mohamed, Shakir; Lakshminarayanan, Balaji: Normalizing flows for probabilistic modeling and inference (2021)
  7. Peluchetti, Stefano; Favaro, Stefano: Doubly infinite residual neural networks: a diffusion process approach (2021)
  8. Roesch, Elisabeth; Rackauckas, Christopher; Stumpf, Michael P. H.: Collocation based training of neural ordinary differential equations (2021)
  9. Sonoda, Sho: Functional analytical methods for neural networks and the infinite-dimensional null space (2021)
  10. Su, Wei-Hung; Chou, Ching-Shan; Xiu, Dongbin: Deep learning of biological models from data: applications to ODE models (2021)
  11. Voloskov, Dmitry; Pissarenko, Dimitri: Adaptive POD-Galerkin technique for reservoir simulation and optimization (2021)
  12. Yin, Yuan; Le Guen, Vincent; Dona, Jérémie; de Bézenac, Emmanuel; Ayed, Ibrahim; Thome, Nicolas; Gallinari, Patrick: Augmenting physical models with deep networks for complex dynamics forecasting (2021)
  13. Brehmer, Johann; Louppe, Gilles; Pavez, Juan; Cranmer, Kyle: Mining gold from implicit models to improve likelihood-free inference (2020)
  14. Chen, Zhen; Wu, Kailiang; Xiu, Dongbin: Methods to recover unknown processes in partial differential equations using data (2020)
  15. Cuchiero, Christa; Larsson, Martin; Teichmann, Josef: Deep neural networks, generic universal interpolation, and controlled ODEs (2020)
  16. Drori, Iddo: Deep variational inference (2020)
  17. E, Weinan; Ma, Chao; Wu, Lei: Machine learning from a continuous viewpoint. I (2020)
  18. Göttlich, Simone; Knapp, Stephan: Artificial neural networks for the estimation of pedestrian interaction forces (2020)
  19. Günther, Stefanie; Ruthotto, Lars; Schroder, Jacob B.; Cyr, Eric C.; Gauger, Nicolas R.: Layer-parallel training of deep residual neural networks (2020)
  20. Jin, Pengzhan; Zhang, Zhen; Zhu, Aiqing; Tang, Yifa; Karniadakis, George Em: Sympnets: intrinsic structure-preserving symplectic networks for identifying Hamiltonian systems (2020)