NeuralPDE.jl

NeuralPDE.jl is a solver package which consists of neural network solvers for partial differential equations using scientific machine learning (SciML) techniques such as physics-informed neural networks (PINNs) and deep BSDE solvers. This package utilizes deep neural networks and neural stochastic differential equations to solve high-dimensional PDEs at a greatly reduced cost and greatly increased generality compared with classical methods.


References in zbMATH (referenced in 23 articles )

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  1. Han Veiga, Maria; Öffner, Philipp; Torlo, Davide: DeC and ADER: similarities, differences and a unified framework (2021)
  2. Hegedűs, Ferenc: Program package MPGOS: challenges and solutions during the integration of a large number of independent ODE systems using GPUs (2021)
  3. Lindner, Michael; Lincoln, Lucas; Drauschke, Fenja; Koulen, Julia M.; Würfel, Hans; Plietzsch, Anton; Hellmann, Frank: NetworkDynamics.jl -- composing and simulating complex networks in Julia (2021)
  4. López, Oscar; Oleaga, Gerardo; Sánchez, Alejandra: Markov-modulated jump-diffusion models for the short rate: pricing of zero coupon bonds and convexity adjustment (2021)
  5. Sinha, Nirvik; Heckman, C. J.; Yang, Yuan: Slowly activating outward membrane currents generate input-output sub-harmonic cross frequency coupling in neurons (2021)
  6. Week, Bob; Nuismer, Scott L.; Harmon, Luke J.; Krone, Stephen M.: A white noise approach to evolutionary ecology (2021)
  7. Wei Peng, Jun Zhang, Weien Zhou, Xiaoyu Zhao, Wen Yao, Xiaoqian Chen: IDRLnet: A Physics-Informed Neural Network Library (2021) arXiv
  8. Antoñana, Mikel; Chartier, Philippe; Makazaga, Joseba; Murua, Ander: Global time-renormalization of the gravitational (N)-body problem (2020)
  9. Bakir, Toufik; Bonnard, Bernard; Bourdin, Loïc; Rouot, Jérémy: Pontryagin-type conditions for optimal muscular force response to functional electrical stimulations (2020)
  10. Haller, George; Karrasch, Daniel; Kogelbauer, Florian: Barriers to the transport of diffusive scalars in compressible flows (2020)
  11. Isensee, Jonas; Datseris, George; Parlitz, Ulrich: Predicting spatio-temporal time series using dimension reduced local states (2020)
  12. Karrasch, Daniel; Schilling, Nathanael: Fast and robust computation of coherent Lagrangian vortices on very large two-dimensional domains (2020)
  13. Mikhlin, Yuri; Onizhuk, Anton: Resonance behavior of the non-ideal system which contains a snap-through truss absorber (2020)
  14. Mikhlin, Yuri; Onizhuk, Anton; Awrejcewicz, Jan: Resonance behavior of the system with a limited power supply having the Mises girder as absorber (2020)
  15. Öffner, Philipp; Torlo, Davide: Arbitrary high-order, conservative and positivity preserving Patankar-type deferred correction schemes (2020)
  16. Ranocha, Hendrik; Ketcheson, David I.: Energy stability of explicit Runge-Kutta methods for nonautonomous or nonlinear problems (2020)
  17. Schilling, Nathanael; Froyland, Gary; Junge, Oliver: Higher-order finite element approximation of the dynamic Laplacian (2020)
  18. Andrade, Tomás; Emparan, Roberto; Licht, David; Luna, Raimon: Cosmic censorship violation in black hole collisions in higher dimensions (2019)
  19. Andrade, Tomás; Emparan, Roberto; Licht, David; Luna, Raimon: Black hole collisions, instabilities, and cosmic censorship violation at large (D) (2019)
  20. Blåbäck, J.; Gautason, F. F.; Ruipérez, A.; Van Riet, T.: Anti-brane singularities as red herrings (2019)

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