Conservative load transfer along curved fluid-solid interface with non-matching meshes. - Rajeev K. Jaiman; Xiangmin Jiao; Philippe H. Geubelle; Eric Loth (J. Comput. Phys., 2006)

Erratum to "Conservative load transfer along curved fluid-solid interface with non-matching meshes" [J. Comput. Phys. 218(1) (2006) 372-397]. - Rajeev K. Jaiman; Xiangmin Jiao; Philippe H. Geubelle; Eric Loth (J. Comput. Phys., 2007)

A stable second-order scheme for fluid-structure interaction with strong added-mass effects. - Jie Liu 003; Rajeev K. Jaiman; Pardha S. Gurugubelli (J. Comput. Phys., 2014)

A positivity preserving variational method for multi-dimensional convection-diffusion-reaction equation. - Vaibhav Joshi; Rajeev K. Jaiman (J. Comput. Phys., 2017)

A positivity preserving and conservative variational scheme for phase-field modeling of two-phase flows. - Vaibhav Joshi; Rajeev K. Jaiman (J. Comput. Phys., 2018)

An adaptive variational procedure for the conservative and positivity preserving Allen-Cahn phase-field model. - Vaibhav Joshi; Rajeev K. Jaiman (J. Comput. Phys., 2018)

A 3D common-refinement method for non-matching meshes in partitioned variational fluid-structure analysis. - Yulong Li; Yun Zhi Law; Vaibhav Joshi; Rajeev K. Jaiman (J. Comput. Phys., 2018)

A variational flexible multibody formulation for partitioned fluid-structure interaction: Application to bat-inspired drones and unmanned air-vehicles. - Vaibhav Joshi; Rajeev K. Jaiman; Carl Ollivier-Gooch (Comput. Math. Appl., 2020)

A robust and accurate finite element framework for cavitating flows with moving fluid-structure interfaces. - Suraj R. Kashyap; Rajeev K. Jaiman (Comput. Math. Appl., 2021)

Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation. - Wrik Mallik; Rajeev K. Jaiman; Jasmin Jelovica (CoRR, 2021)

A variational interface-preserving and conservative phase-field method for the surface tension effect in two-phase flows. - Xiaoyu Mao; Vaibhav Joshi; Rajeev K. Jaiman (J. Comput. Phys., 2021)

Deep convolutional neural network for shape optimization using level-set approach. - Wrik Mallik; Neil Farvolden; Jasmin Jelovica; Rajeev K. Jaiman (CoRR, 2022)

Learning Wave Propagation with Attention-Based Convolutional Recurrent Autoencoder Net. - Indu Kant Deo; Rajeev Jaiman (CoRR, 2022)

Convolutional recurrent autoencoder network for learning underwater ocean acoustics. - Wrik Mallik; Rajeev K. Jaiman; Jasmin Jelovica (CoRR, 2022)

Quasi-Monolithic Graph Neural Network for Fluid-Structure Interaction. - Rui Gao; Rajeev Jaiman (CoRR, 2022)

Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics. - Indu Kant Deo; Rui Gao; Rajeev Jaiman (CoRR, 2022)

Node-Element Hypergraph Message Passing for Fluid Dynamics Simulations. - Rui Gao; Indu Kant Deo; Rajeev K. Jaiman (CoRR, 2022)

An interface and geometry preserving phase-field method for fully Eulerian fluid-structure interaction. - Xiaoyu Mao; Rajeev Jaiman (J. Comput. Phys., 2023)

An interface preserving and residual-based adaptivity for phase-field modeling of fully Eulerian fluid-structure interaction. - Biswajeet Rath; Xiaoyu Mao; Rajeev K. Jaiman (J. Comput. Phys., 2023)

Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net. - Indu Kant Deo; Akash Venkateshwaran; Rajeev K. Jaiman (CoRR, 2024)

H-SIREN: Improving implicit neural representations with hyperbolic periodic functions. - Rui Gao; Rajeev K. Jaiman (CoRR, 2024)

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks. - Indu Kant Deo; Rajeev Jaiman (CoRR, 2024)

A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations. - Rui Gao; Indu Kant Deo; Rajeev K. Jaiman (J. Comput. Phys., 2024)

Data-driven modeling of fluid flow around rotating structures with graph neural networks. - Rui Gao; Zhi Cheng; Rajeev K. Jaiman (CoRR, 2025)

Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition. - Indu Kant Deo; Rajeev K. Jaiman (CoRR, 2025)

A Reward-Directed Diffusion Framework for Generative Design Optimization. - Hadi Keramati; Patrick Kirchen; Mohammed Hannan; Rajeev K. Jaiman (CoRR, 2025)