6 papers
Modeling the Impact of Fiber Latency on Compute-Communication Overlap in Geo-Distributed Multi-Datacenter AI Training
Ioannis Papavasileiou, Sairam Prabhakar, Indu Kant Deo +1
We use discrete-event simulation to quantify the impact of fiber latency on the efficacy of geo-distributed AI model training with data parallelism. We conclude that the optimum di…
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…
MUTE-DSS: A Digital-Twin-Based Decision Support System for Minimizing Underwater Radiated Noise in Ship Voyage Planning
Akash Venkateshwaran, Indu Kant Deo, Rajeev K. Jaiman
We present a novel MUTE-DSS, a digital-twin-based decision support system for minimizing underwater radiated noise (URN) during ship voyage planning. It is a ROS2-centric framework…
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…
Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing
Indu Kant Deo, Youngsoo Choi, Saad A. Khairallah +2
In Laser Powder Bed Fusion (LPBF), the applied laser energy produces high thermal gradients that lead to unacceptable final part distortion. Accurate distortion prediction is essen…
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…