180 citations · 944 across the 24 of their papers we have counts for
6 papers · 1 filter
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…
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…
Physics guided machine learning using simplified theories
Suraj Pawar, Omer San, Burak Aksoylu +2
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences…