180 citations · 944 across the 24 of their papers we have counts for
9 papers · 1 filter
Numerical assessments of a nonintrusive surrogate model based on recurrent neural networks and proper orthogonal decomposition: Rayleigh Benard convection
Saeed Akbari, Suraj Pawar, Omer San
Recent developments in diagnostic and computing technologies offer to leverage numerous forms of nonintrusive modeling approaches from data where machine learning can be used to bu…
Prospects of federated machine learning in fluid dynamics
Omer San, Suraj Pawar, Adil Rasheed
Physics-based models have been mainstream in fluid dynamics for developing predictive models. In recent years, machine learning has offered a renaissance to the fluid community due…
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
Omer San, Suraj Pawar, Adil Rasheed
A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained represen…
Decentralized digital twins of complex dynamical systems
Omer San, Suraj Pawar, Adil Rasheed
In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and e…
Equation-free surrogate modeling of geophysical flows at the intersection of machine learning and data assimilation
Suraj Pawar, Omer San
There is a growing interest in developing data-driven reduced-order models for atmospheric and oceanic flows that are trained on data obtained either from high-resolution simulatio…
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…
Multi-fidelity reinforcement learning framework for shape optimization
Sahil Bhola, Suraj Pawar, Prasanna Balaprakash +1
Deep reinforcement learning (DRL) is a promising outer-loop intelligence paradigm which can deploy problem solving strategies for complex tasks. Consequently, DRL has been utilized…
Frame invariant neural network closures for Kraichnan turbulence
Suraj Pawar, Omer San, Adil Rasheed +1
Numerical simulations of geophysical and atmospheric flows have to rely on parameterizations of subgrid scale processes due to their limited spatial resolution. Despite substantial…
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…