Rajeev Jaiman

Professor

B. Tech. (IIT Bombay), M.S., Ph.D. (U of Illinois, Urbana-Champaign, Aerospace Engineering), Senior Member AIAA, Member APS, ASME, SNAME, USACM, NSERC/Seaspan Industrial Research Chair in Intelligent and Green Marine Vessels (IGMVs), SNAME Fellow

Research Interests

• Fluid-Structure Interaction
• Marine Hydrodynamics
• Scientific Machine Learning
• Digital Twins and Physics-guided AI
• Intelligent Ocean Systems
• Multiphase Flows and Cavitation

Current Research Work

Engineering is entering an era in which computational physics and artificial intelligence are converging to transform the design, operation, and management of complex engineering systems. My research program advances high-fidelity multiphysics and physics-guided AI methodologies that enable this transformation.  We work closely with industrial partners, government agencies, defence and security stakeholders, and Indigenous communities to translate advances in computational science into deployable engineering technologies. These technologies aim to improve efficiency, reduce emissions, predict underwater acoustics, strengthen maritime security, and enable next-generation intelligent marine and subsea systems. The long-term vision of my research is to establish high-fidelity multiphysics and physics-guided AI as foundational technologies for predictive digital engineering, enabling intelligent marine and subsea systems with reliable prediction, optimal design, safe autonomous operation, environmental sustainability, and national security relevance. Further highlights of ongoing research themes are as follows:

• Computational Multiphysics and Fluid-Structure Interaction: We develop predictive computational frameworks for strongly coupled fluid-structure interaction, multiphase flows, moving interfaces, cavitation, contact mechanics, and multidomain systems. Our research advances scalable numerical methods capable of accurately resolving complex multiphysics / multiscale interactions in marine, offshore, aerospace, and bio-inspired engineering and robotics applications.
• Scientific Machine Learning and Digital Twins: We develop physics-guided AI methodologies for predictive modeling, real-time decision support, and digital engineering. Current methods include hypergraph neural networks, numerical integration-inspired attention mechanism, probabilistic surrogates and generative AI for engineering design. These methods accelerate high-fidelity simulations and enable predictive digital twins for monitoring, optimization, uncertainty quantification, and autonomous control.
• Marine Hydrodynamics and Underwater Acoustics: We investigate ship and submarine hydrodynamics, propeller cavitation, vortex dynamics, underwater acoustics, underwater radiated noise, and wave propagation in open-water and ice-covered environments. A major objective is to develop predictive technologies that improve the performance, stealth, safety, and environmental sustainability of marine and subsea systems operating in Arctic and offshore environments.
• Intelligent Marine and Subsea Systems: We develop intelligent ocean technologies through digital twins, edge AI, sensor-informed prediction, underwater acoustic modeling, and cooperative marine and subsea robotic systems. Our research supports autonomous surface and underwater vehicles, resilient subsea infrastructure, and intelligent decision-support systems for safe, efficient, and environmentally responsible marine and Arctic operations.
• Bio-inspired Fluid Mechanics and Flexible Structures: We investigate flexible morphing wings, hydrofoils, flapping elastic structures, bio-inspired propulsion, and autonomous robotic systems. Our research seeks to understand flow-induced vibration, aeroelastic and hydroelastic instabilities, energy harvesting, and efficient locomotion to inspire the next generation of aerial, marine, and subsea vehicles.

Selected Publications

• Deo, I. K., Venkateshwaran, A., Jaiman, R.K. A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise, Ocean Engineering, vol. 358, 125461, 2026.
• Venkateshwaran, A., Deo, I. K. & Jaiman, R.K. MUTE-DSS: A digital-twin-based decision support system for minimizing underwater radiated noise in ship voyage planning, Ocean Engineering, vol. 343, pp. 123343, 2026.
• Rath, B., Jaiman, R.K. A phase-field formulation of frictional sliding contact for 3D fully Eulerian fluid-structure interactions, Computer Methods in Applied Mechanics and Engineering, 451, 118685, 2026.
• Keramati, H., Kirchen, P., Hannan, M., Jaiman, R.K. A reward-directed diffusion framework for generative design, Engineering Applications of Artificial Intelligence, 165, 113378, 2026.
• Parekh, A.R., Gao, R., Jaiman, R.K. An efficient and accurate surrogate modeling of flapping dynamics in inverted elastic foils using hypergraph neural networks, International Journal of Heat and Fluid Flow, 119, 110316, 2026.
• Keramati, H., Sadeghi, M., Jaiman, R.K. HeatGen: A guided diffusion framework for multiphysics heat sink design optimization, International Journal of Heat and Mass Transfer, 261, 128579, 2026.
• Heydari, S., Gao, R., Jaiman, R.K. Predicting Flow-Induced Vibration in Isolated and Tandem Cylinders Using Hypergraph Neural Networks, Computers & Fluids, 106930, 2025.
• Deo, I., Venkateshwaran, A., & Jaiman, R.K. Predicting transmission loss in underwater acoustics using continual learning with range-dependent conditional convolutional neural networks, The Journal of the Acoustical Society of America, 157 (5), 3930-3945, 2025.
• Lak, S. & Jaiman, R.K. Suppressing tip vortex cavitation through passive deformation of a hydrofoil. I. Bending, Physics of Fluids, 37 (8), 083379, 2025.
• Mallik, W., Jelovica, J., Jaiman, R.K. Shape optimization for fluid flow with parametric level set method and deep neural networks, Computers & Fluids, 295, 106626, 2025.
• Wong, J.C.M., Joshi, V., Jaiman, R.K., Altshuler, D. Wing extension–flexion coupled aeroelastic effects improve avian gliding performance, Journal of Royal Society Interface, 22: 20240753, 2025.
• Hadizadeh, F., Mallik, W., Jaiman, R.K. A Graph Neural Network Surrogate Model for Multi-Objective Fluid-Acoustic Shape Optimization, Computer Methods in Applied Mechanics and Engineering, 441 (117921), 2025.
• Cheng, Z., Darbhamulla, N.B., Jaiman, R.K. Flow-induced vibration of flexible tapering hydrofoils with and without sheet cavitation, Int. Journal of Multiphase Flow, 105149, 2025.
• Li, G., Zhang, H., Lei, B., Jiang, W., Liu, H., Jaiman, R.K. Aeroelastic characteristics of multi-segment thin flexible structures with deflected flaps, Ocean Engineering, 321, 120374, 2025.
• Heydari, S. and Jaiman, R.K. Flow-induced vibration of a flexible cantilever in tandem configuration, Physical Review Fluids, 10 (2), 024701, 2025.
• Cheng, Z., Smoker, B., Kashyap, S., Burella, G., Jaiman, R.K. Cavitating wake dynamics and hydroacoustics performance of marine propeller with a nozzle, Physics of Fluids, 37 (1), 2025.
• Parekh, A.R. and Jaiman, R.K. Wake interference effects on flapping dynamics of elastic inverted foil, Physical Review Fluids, 10 (1), 014702, 2025.

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