106 citations · 883 across the 63 of their papers we have counts for
6 papers · 1 filter
HeatGen: A Guided Diffusion Framework for Multiphysics Heat Sink Design Optimization
Hadi Keramati, Morteza Sadeghi, Rajeev K. Jaiman
This study presents a generative optimization framework based on a guided denoising diffusion probabilistic model (DDPM) that leverages surrogate gradients to generate heat sink de…
A Physics-Guided Probabilistic Surrogate Modeling Framework for Digital Twins of Underwater Radiated Noise
Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
Ship traffic is an increasing source of underwater radiated noise in coastal waters, motivating real-time digital twins of ocean acoustics for operational noise mitigation. We pres…
Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition
Indu Kant Deo, Rajeev K. Jaiman
In this paper, we present a physics-based deep learning framework for data-driven prediction of wave propagation in fluid media. The proposed approach, termed Multistep Integration…
Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks
Indu Kant Deo, Rajeev Jaiman
Accurate prediction over long time horizons is crucial for modeling complex physical processes such as wave propagation. Although deep neural networks show promise for real-time fo…
Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net
Indu Kant Deo, Akash Venkateshwaran, Rajeev K. Jaiman
There is a significant need for precise and reliable forecasting of the far-field noise emanating from shipping vessels. Conventional full-order models based on the Navier-Stokes e…
Kinematically consistent recurrent neural networks for learning inverse problems in wave propagation
Wrik Mallik, Rajeev K. Jaiman, Jasmin Jelovica
Although machine learning (ML) is increasingly employed recently for mechanistic problems, the black-box nature of conventional ML architectures lacks the physical knowledge to inf…