147 citations · 396 across the 9 of their papers we have counts for
18 papers
Physics guided neural networks for modelling of non-linear dynamics
Haakon Robinson, Suraj Pawar, Adil Rasheed +1
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns fro…
Hyperparameter Search using Genetic Algorithm for Surrogate Modeling of Geophysical Flows
Suraj Pawar, Omer San, Gary G. Yen
The computational models for geophysical flows are computationally very expensive to employ in multi-query tasks such as data assimilation, uncertainty quantification, and hence su…
Multi-fidelity information fusion with concatenated neural networks
Suraj Pawar, Omer San, Prakash Vedula +2
Recently, computational modeling has shifted towards the use of deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many appl…
On closures for reduced order models A spectrum of first-principle to machine-learned avenues
Shady E. Ahmed, Suraj Pawar, Omer San +3
For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fluid mechanics. Early examples include Galerkin models inspired by the Orr-Sommer…
A nonintrusive hybrid neural-physics modeling of incomplete dynamical systems: Lorenz equations
Suraj Pawar, Omer San, Adil Rasheed +1
This work presents a hybrid modeling approach to data-driven learning and representation of unknown physical processes and closure parameterizations. These hybrid models are suitab…
Hybrid analysis and modeling for next generation of digital twins
Suraj Pawar, Shady E. Ahmed, Omer San +1
The physics-based modeling has been the workhorse for many decades in many scientific and engineering applications ranging from wind power, weather forecasting, and aircraft design…