5 citations · 18 across the 9 of their papers we have counts for
9 papers
Online Relational Inference for Evolving Multi-agent Interacting Systems
Beomseok Kang, Priyabrata Saha, Sudarshan Sharma +2
We introduce a novel framework, Online Relational Inference (ORI), designed to efficiently identify hidden interaction graphs in evolving multi-agent interacting systems using stre…
Bridging Autoencoders and Dynamic Mode Decomposition for Reduced-order Modeling and Control of PDEs
Priyabrata Saha, Saibal Mukhopadhyay
Modeling and controlling complex spatiotemporal dynamical systems driven by partial differential equations (PDEs) often necessitate dimensionality reduction techniques to construct…
RADNet: A Deep Neural Network Model for Robust Perception in Moving Autonomous Systems
Burhan A. Mudassar, Sho Ko, Maojingjing Li +2
Interactive autonomous applications require robustness of the perception engine to artifacts in unconstrained videos. In this paper, we examine the effect of camera motion on the t…
Unraveled Multilevel Transformation Networks for Predicting Sparsely-Observed Spatiotemporal Dynamics
Priyabrata Saha, Saibal Mukhopadhyay
In this paper, we address the problem of predicting complex, nonlinear spatiotemporal dynamics when available data is recorded at irregularly-spaced sparse spatial locations. Most…
A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data
Priyabrata Saha, Saibal Mukhopadhyay
In this paper, we consider the problem of learning prediction models for spatiotemporal physical processes driven by unknown partial differential equations (PDEs). We propose a dee…
Neural Identification for Control
Priyabrata Saha, Magnus Egerstedt, Saibal Mukhopadhyay
We present a new method for learning control law that stabilizes an unknown nonlinear dynamical system at an equilibrium point. We formulate a system identification task in a self-…
Physics-Incorporated Convolutional Recurrent Neural Networks for Source Identification and Forecasting of Dynamical Systems
Priyabrata Saha, Saurabh Dash, Saibal Mukhopadhyay
Spatio-temporal dynamics of physical processes are generally modeled using partial differential equations (PDEs). Though the core dynamics follows some principles of physics, real-…
MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network
Priyabrata Saha, Arslan Ali, Burhan A. Mudassar +2
We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observatio…
Mixture of Pre-processing Experts Model for Noise Robust Deep Learning on Resource Constrained Platforms
Taesik Na, Minah Lee, Burhan A. Mudassar +3
Deep learning on an edge device requires energy efficient operation due to ever diminishing power budget. Intentional low quality data during the data acquisition for longer batter…