Siddharth Rout
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View article: Fast, Convex and Conditioned Network for Multi-Fidelity Vectors and Stiff Univariate Differential Equations
Fast, Convex and Conditioned Network for Multi-Fidelity Vectors and Stiff Univariate Differential Equations Open
Accuracy in neural PDE solvers often breaks down not because of limited expressivity, but due to poor optimisation caused by ill-conditioning, especially in multi-fidelity and stiff problems. We study this issue in Physics-Informed Extreme…
View article: Advection Augmented Convolutional Neural Networks
Advection Augmented Convolutional Neural Networks Open
Many problems in physical sciences are characterized by the prediction of space-time sequences. Such problems range from weather prediction to the analysis of disease propagation and video prediction. Modern techniques for the solution of …
View article: Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets
Acoustics-based Active Control of Unsteady Flow Dynamics using Reinforcement Learning Driven Synthetic Jets Open
This study proposes the use of deep reinforcement learning (DRL) to actively control wakes and noise from flow past a cylinder by leveraging acoustic-based pressure feedback. A hydrophone array captures downstream signals, enabling a DRL a…
View article: Early Advancements in Turbulence-Generated Noise Modelling: A Review
Early Advancements in Turbulence-Generated Noise Modelling: A Review Open
Turbulent flows generate a broadband of acoustic noise, which can be extremely important. So, there is need for modelling the generation and propagation of acoustic energy in fluid flows, especially turbulent. This chapter reviews the rese…
View article: PINN for Dynamical Partial Differential Equations is Not Training Deeper Networks Rather Learning Advection and Time Variance
PINN for Dynamical Partial Differential Equations is Not Training Deeper Networks Rather Learning Advection and Time Variance Open
The concepts and techniques of physics-informed neural networks (PINNs) is studied and limitations are identified to make it efficient to approximate dynamical equations. Potential working research domains are explored for increasing the r…
View article: Airfoil Shape Optimization using Deep Q-Network
Airfoil Shape Optimization using Deep Q-Network Open
The feasibility of using reinforcement learning for airfoil shape optimization is explored. Deep Q-Network (DQN) is used over Markov's decision process to find the optimal shape by learning the best changes to the initial shape for achievi…
View article: Numerical Approximation in CFD Problems Using Physics Informed Machine Learning
Numerical Approximation in CFD Problems Using Physics Informed Machine Learning Open
The thesis focuses on various techniques to find an alternate approximation method that could be universally used for a wide range of CFD problems but with low computational cost and low runtime. Various techniques have been explored withi…
View article: Numerical Approximation in CFD Problems Using Physics Informed Machine\n Learning
Numerical Approximation in CFD Problems Using Physics Informed Machine\n Learning Open
The thesis focuses on various techniques to find an alternate approximation\nmethod that could be universally used for a wide range of CFD problems but with\nlow computational cost and low runtime. Various techniques have been explored\nwi…
View article: Quantitative Expression Analysis of Circulating miRNAs Reveals Significant Association with Cardiovascular Pathogenesis in Women
Quantitative Expression Analysis of Circulating miRNAs Reveals Significant Association with Cardiovascular Pathogenesis in Women Open
Background Cardiovascular disease (CVD) represents a complex pathophysiological condition with continuous increasing mortality and poor treatment outcome. Troponin is the only standard biochemical indication of CVD, which fails to exactly …