Off-Road Lane Detection Using Superpixel Clustering and RANSAC Curve Fitting. - Sanskar Agrawal; Indu Kant Deo; Siddhant Haldar; G. Rahul Kranti Kiran; Vaibhav Lodhi; Debashish Chakravarty (ICARCV, 2018)

Design and Implementation of Autonomous Ground Vehicle for Constrained Environments. - G. Rahul Kranti Kiran; Indu Kant Deo; Sanskar Agrawal; Siddhant Haldar; Het Shah; Sohan Rudra; Harsh Maheshwari; Aditya Rathore; Poojan Shah; Ashwin Nehete; Debashish Chakravarty (IRC, 2019)

Learning Wave Propagation with Attention-Based Convolutional Recurrent Autoencoder Net. - Indu Kant Deo; Rajeev Jaiman (CoRR, 2022)

Combined space-time reduced-order model with 3D deep convolution for extrapolating fluid dynamics. - Indu Kant Deo; Rui Gao; Rajeev Jaiman (CoRR, 2022)

Node-Element Hypergraph Message Passing for Fluid Dynamics Simulations. - Rui Gao; Indu Kant Deo; Rajeev K. Jaiman (CoRR, 2022)

Continual Learning of Range-Dependent Transmission Loss for Underwater Acoustic using Conditional Convolutional Neural Net. - Indu Kant Deo; Akash Venkateshwaran; Rajeev K. Jaiman (CoRR, 2024)

Harnessing Loss Decomposition for Long-Horizon Wave Predictions via Deep Neural Networks. - Indu Kant Deo; Rajeev Jaiman (CoRR, 2024)

Data-Driven, Parameterized Reduced-order Models for Predicting Distortion in Metal 3D Printing. - Indu Kant Deo; Youngsoo Choi; Saad A. Khairallah; Alexandre Reikher; Maria Strantza (CoRR, 2024)

A finite element-inspired hypergraph neural network: Application to fluid dynamics simulations. - Rui Gao; Indu Kant Deo; Rajeev K. Jaiman (J. Comput. Phys., 2024)

Predicting Wave Dynamics using Deep Learning with Multistep Integration Inspired Attention and Physics-Based Loss Decomposition. - Indu Kant Deo; Rajeev K. Jaiman (CoRR, 2025)