no code implementations • 15 Aug 2022 • Omer San, Suraj Pawar, Adil Rasheed
Physics-based models have been mainstream in fluid dynamics for developing predictive models.
no code implementations • 7 Jul 2022 • 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 engineering applications.
no code implementations • 7 Jul 2022 • 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 representations due to underlying highly nonlinear multiscale interactions.
no code implementations • 13 May 2022 • Haakon Robinson, Suraj Pawar, Adil Rasheed, Omer San
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 from large datasets with minimal human intervention.
no code implementations • 22 Feb 2022 • Sahil Bhola, Suraj Pawar, Prasanna Balaprakash, Romit Maulik
One key limitation of conventional DRL methods is their episode-hungry nature which proves to be a bottleneck for tasks which involve costly evaluations of a numerical forward model.
1 code implementation • 18 Dec 2020 • Suraj Pawar, Omer San, Burak Aksoylu, Adil Rasheed, Trond Kvamsdal
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences.
no code implementations • 5 Aug 2020 • Shady Ahmed, Suraj Pawar, Omer San, Adil Rasheed, Mandar Tabib
We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements.
no code implementations • 28 May 2020 • Shady Ahmed, Suraj Pawar, Omer San, Adil Rasheed
In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements.
Dynamical Systems Computational Physics Fluid Dynamics