18 citations · 43 across the 7 of their papers we have counts for
4 papers · 1 filter
A novel corrective-source term approach to modeling unknown physics in aluminum extraction process
Haakon Robinson, Erlend Lundby, Adil Rasheed +1
With the ever-increasing availability of data, there has been an explosion of interest in applying modern machine learning methods to fields such as modeling and control. However,…
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…