Search Results - "Communications in computational physics"

Refine Results
  1. 1

    Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations by Ameya D. Jagtap, Ameya D. Jagtap, George Em Karniadakis, George Em Karniadakis

    Published in Communications in computational physics (01-11-2020)
    “…Here we propose a generalized space-time domain decomposition approach for the physics-informed neural networks (PINNs) to solve nonlinear partial differential…”
    Get full text
    Journal Article
  2. 2

    On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs by Yeonjong Shin, Yeonjong Shin, Jérôme Darbon, Jérôme Darbon, George Em Karniadakis, George Em Karniadakis

    Published in Communications in computational physics (01-11-2020)
    “…Physics informed neural networks (PINNs) are deep learning based techniques for solving partial differential equations (PDEs) encountered in computational…”
    Get full text
    Journal Article
  3. 3
  4. 4

    Fast Evaluation of the Caputo Fractional Derivative and its Applications to Fractional Diffusion Equations by Jiang, Shidong, Zhang, Jiwei, Zhang, Qian, Zhang, Zhimin

    Published in Communications in computational physics (01-03-2017)
    “…The computational work and storage of numerically solving the time fractional PDEs are generally huge for the traditional direct methods since they require…”
    Get full text
    Journal Article
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14

    Fast Evaluation of the Caputo Fractional Derivative and its Applications to Fractional Diffusion Equations: A Second-Order Scheme by Yan, Yonggui, Sun, Zhi-Zhong, Zhang, Jiwei

    Published in Communications in computational physics (01-10-2017)
    “…The fractional derivatives include nonlocal information and thus their calculation requires huge storage and computational cost for long time simulations. We…”
    Get full text
    Journal Article
  15. 15

    Numerical Methods for Fluid-Structure Interaction — A Review by Hou, Gene, Wang, Jin, Layton, Anita

    Published in Communications in computational physics (01-08-2012)
    “…The interactions between incompressible fluid flows and immersed structures are nonlinear multi-physics phenomena that have applications to a wide range of…”
    Get full text
    Journal Article
  16. 16
  17. 17

    Phase-Field Models for Multi-Component Fluid Flows by Kim, Junseok

    Published in Communications in computational physics (01-09-2012)
    “…In this paper, we review the recent development of phase-field models and their numerical methods for multi-component fluid flows with interfacial phenomena…”
    Get full text
    Journal Article
  18. 18

    A Finite-Volume Method for Nonlinear Nonlocal Equations with a Gradient Flow Structure by Carrillo, José A., Chertock, Alina, Huang, Yanghong

    Published in Communications in computational physics (01-01-2015)
    “…We propose a positivity preserving entropy decreasing finite volume scheme for nonlinear nonlocal equations with a gradient flow structure. These properties…”
    Get full text
    Journal Article
  19. 19
  20. 20