294 citations · 2.9k across the 83 of their papers we have counts for
8 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…
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
Sindre Stenen Blakseth, Adil Rasheed, Trond Kvamsdal +1
Upcoming technologies like digital twins, autonomous, and artificial intelligent systems involving safety-critical applications require models which are accurate, interpretable, co…
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…
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…