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 86 articles )

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  1. Chen, Yuyan; Dong, Bin; Xu, Jinchao: Meta-mgnet: meta multigrid networks for solving parameterized partial differential equations (2022)
  2. Efendiev, Yalchin; Leung, Wing Tat; Lin, Guang; Zhang, Zecheng: Efficient hybrid explicit-implicit learning for multiscale problems (2022)
  3. E, Weinan; Han, Jiequn; Jentzen, Arnulf: Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning (2022)
  4. Gao, Liyao; Du, Yifan; Li, Hongshan; Lin, Guang: RotEqNet: rotation-equivariant network for fluid systems with symmetric high-order tensors (2022)
  5. García, Constantino A.; Félix, Paulo; Presedo, Jesús M.; Otero, Abraham: Stochastic embeddings of dynamical phenomena through variational autoencoders (2022)
  6. Göttlich, Simone; Totzeck, Claudia: Parameter calibration with stochastic gradient descent for interacting particle systems driven by neural networks (2022)
  7. Herty, Michael; Trimborn, Torsten; Visconti, Giuseppe: Mean-field and kinetic descriptions of neural differential equations (2022)
  8. Hofmann, S.; Borzì, A.: A sequential quadratic Hamiltonian algorithm for training explicit RK neural networks (2022)
  9. Huot, Mathieu; Staton, Sam; Vákár, Matthijs: Higher order automatic differentiation of higher order functions (2022)
  10. Hu, Pipi; Yang, Wuyue; Zhu, Yi; Hong, Liu: Revealing hidden dynamics from time-series data by ODENet (2022)
  11. Kim, Youngkyu; Choi, Youngsoo; Widemann, David; Zohdi, Tarek: A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder (2022)
  12. Li, Jingshi; Chen, Song; Wang, Lijin; Cao, Yanzhao: A symplectic based neural network algorithm for quantum controls under uncertainty (2022)
  13. Margenberg, Nils; Hartmann, Dirk; Lessig, Christian; Richter, Thomas: A neural network multigrid solver for the Navier-Stokes equations (2022)
  14. Meng, Pinchao; Wang, Xinyu; Yin, Weishi: ODE-RU: a dynamical system view on recurrent neural networks (2022)
  15. Oliva, Paul Valsecchi; Wu, Yue; He, Cuiyu; Ni, Hao: Towards fast weak adversarial training to solve high dimensional parabolic partial differential equations using XNODE-WAN (2022)
  16. Ren, Pu; Rao, Chengping; Liu, Yang; Wang, Jian-Xun; Sun, Hao: PhyCRNet: physics-informed convolutional-recurrent network for solving spatiotemporal PDEs (2022)
  17. Ruiz-Balet, Domènec; Affili, Elisa; Zuazua, Enrique: Interpolation and approximation via momentum ResNets and neural ODEs (2022)
  18. Sokolowski, Jan; Schulz, Volker; Beise, Hans-Peter; Schroeder, Udo: A hybrid objective function for robustness of artificial neural networks -- estimation of parameters in a mechanical system (2022)
  19. Tac, Vahidullah; Sahli Costabal, Francisco; Tepole, Adrian B.: Data-driven tissue mechanics with polyconvex neural ordinary differential equations (2022)
  20. Tang, Kejun; Wan, Xiaoliang; Liao, Qifeng: Adaptive deep density approximation for Fokker-Planck equations (2022)

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