Search Results for author: Suraj Pawar

Found 8 papers, 1 papers with code

Prospects of federated machine learning in fluid dynamics

no code implementations15 Aug 2022 Omer San, Suraj Pawar, Adil Rasheed

Physics-based models have been mainstream in fluid dynamics for developing predictive models.

Decentralized digital twins of complex dynamical systems

no code implementations7 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.

BIG-bench Machine Learning Federated Learning

Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems

no code implementations7 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.

Reinforcement Learning (RL)

Physics guided neural networks for modelling of non-linear dynamics

no code implementations13 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.

Multi-fidelity reinforcement learning framework for shape optimization

no code implementations22 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.

reinforcement-learning Reinforcement Learning (RL) +1

Physics guided machine learning using simplified theories

1 code implementation18 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.

BIG-bench Machine Learning

A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction

no code implementations5 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.

Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning

no code implementations28 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

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