A5034212739- Fluid Dynamics and Vibration Analysis
- Model Reduction and Neural Networks
- Fluid Dynamics and Turbulent Flows
- Gas Dynamics and Kinetic Theory
- Wind and Air Flow Studies
- Nanofluid Flow and Heat Transfer
- Computational Fluid Dynamics and Aerodynamics
- Quantum, superfluid, helium dynamics
- Real-time simulation and control systems
- Probabilistic and Robust Engineering Design
Max Planck Institute for Dynamics of Complex Technical Systems
2023
RWTH Aachen University
2017-2020
Indian Institute of Technology Guwahati
2017
Indian Institute of Science Bangalore
2017
When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on order reduction desirable. In absence the governing equations describing dynamics, we need to construct parametric reduced-order in non-intrusive fashion. this setting, usual residual-based error estimate optimal sampling associated with reduced basis method is not directly available. Our work provides error-estimator-based optimality criterion...
Partial differential equation parameter estimation is a mathematical and computational process used to estimate the unknown parameters in partial model from observational data. This paper employs greedy sampling approach based on Discrete Empirical Interpolation Method identify most informative samples dataset associated with its parameters. Greedy are train physics-informed neural network architecture which maps nonlinear relation between spatio-temporal data measured values. To prove...
In this paper, force convective flow and heat transfer characteristics past an unconfined blunt headed cylinder has been computed for various ranges of Reynolds Prandtl numbers. The mathematical model is first validated with the available results from literature are found to be in good agreement. boundary layer separation local overall rates determined numerical simulations. For present Re Pr, empirical correlation proposed average Nusselt number as: Nuavg = 0.574 Re0.359Pr0.465. Of...
When repeated evaluations for varying parameter configurations of a high-fidelity physical model are required, surrogate modeling techniques based on order reduction desired. In absence the governing equations describing dynamics, we need to construct parametric reduced-order in non-intrusive fashion. this setting, usual residual-based error estimate optimal sampling associated with reduced basis method is not directly available. Our work provides optimality criterion efficiently populate...