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Rajeev K. Jaiman
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2020 – today
- 2024
- [j13]Rui Gao
, Indu Kant Deo, Rajeev K. Jaiman
A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations. J. Comput. Phys. 504: 112866 (2024) - [i9]Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman:
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net. CoRR abs/2404.08091 (2024) - [i8]Rui Gao, Rajeev K. Jaiman:
H-SIREN: Improving implicit neural representations with hyperbolic periodic functions. CoRR abs/2410.04716 (2024) - 2023
- [j12]Xiaoyu Mao, Rajeev Jaiman
An interface and geometry preserving phase-field method for fully Eulerian fluid-structure interaction. J. Comput. Phys. 476: 111903 (2023) - [j11]Biswajeet Rath
An interface preserving and residual-based adaptivity for phase-field modeling of fully Eulerian fluid-structure interaction. J. Comput. Phys. 488: 112188 (2023) - 2022
- [i7]Wrik Mallik, Neil Farvolden, Jasmin Jelovica, Rajeev K. Jaiman:
Deep convolutional neural network for shape optimization using level-set approach. CoRR abs/2201.06210 (2022) - [i6]Indu Kant Deo, Rajeev Jaiman:
Learning Wave Propagation with Attention-Based Convolutional Recurrent Autoencoder Net. CoRR abs/2201.06628 (2022) - [i5]Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica:
Convolutional recurrent autoencoder network for learning underwater ocean acoustics. CoRR abs/2204.05573 (2022) - [i4]Rui Gao, Rajeev Jaiman:
Quasi-Monolithic Graph Neural Network for Fluid-Structure Interaction. CoRR abs/2210.04193 (2022) - [i3]Indu Kant Deo, Rui Gao, Rajeev Jaiman:
Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics. CoRR abs/2211.00307 (2022) - [i2]Rui Gao, Indu Kant Deo, Rajeev K. Jaiman:
Node-Element Hypergraph Message Passing for Fluid Dynamics Simulations. CoRR abs/2212.14545 (2022) - 2021
- [j10]Suraj R. Kashyap
A robust and accurate finite element framework for cavitating flows with moving fluid-structure interfaces. Comput. Math. Appl. 103: 19-39 (2021) - [j9]Xiaoyu Mao
A variational interface-preserving and conservative phase-field method for the surface tension effect in two-phase flows. J. Comput. Phys. 433: 110166 (2021) - [i1]Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica:
Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation. CoRR abs/2110.03903 (2021) - 2020
- [j8]Vaibhav Joshi, Rajeev K. Jaiman, Carl Ollivier-Gooch:
A variational flexible multibody formulation for partitioned fluid-structure interaction: Application to bat-inspired drones and unmanned air-vehicles. Comput. Math. Appl. 80(12): 2707-2737 (2020)
2010 – 2019
- 2018
- [j7]Vaibhav Joshi, Rajeev K. Jaiman
A positivity preserving and conservative variational scheme for phase-field modeling of two-phase flows. J. Comput. Phys. 360: 137-166 (2018) - [j6]Vaibhav Joshi, Rajeev K. Jaiman
An adaptive variational procedure for the conservative and positivity preserving Allen-Cahn phase-field model. J. Comput. Phys. 366: 478-504 (2018) - [j5]Yulong Li, Yun Zhi Law
A 3D common-refinement method for non-matching meshes in partitioned variational fluid-structure analysis. J. Comput. Phys. 374: 163-187 (2018) - 2017
- [j4]Vaibhav Joshi, Rajeev K. Jaiman
A positivity preserving variational method for multi-dimensional convection-diffusion-reaction equation. J. Comput. Phys. 339: 247-284 (2017) - 2014
- [j3]Jie Liu
A stable second-order scheme for fluid-structure interaction with strong added-mass effects. J. Comput. Phys. 270: 687-710 (2014)
2000 – 2009
- 2007
- [j2]Rajeev K. Jaiman, Xiangmin Jiao
Erratum to "Conservative load transfer along curved fluid-solid interface with non-matching meshes" [J. Comput. Phys. 218(1) (2006) 372-397]. J. Comput. Phys. 224(2): 1307 (2007) - 2006
- [j1]Rajeev K. Jaiman, Xiangmin Jiao
Conservative load transfer along curved fluid-solid interface with non-matching meshes. J. Comput. Phys. 218(1): 372-397 (2006)
Coauthor Index
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