5 citations · 18 across the 9 of their papers we have counts for
4 papers · 1 filter
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…