11 citations · 18 across the 4 of their papers we have counts for
6 papers
FRIGATE: Frugal Spatio-temporal Forecasting on Road Networks
Mridul Gupta, Hariprasad Kodamana, Sayan Ranu
Modelling spatio-temporal processes on road networks is a task of growing importance. While significant progress has been made on developing spatio-temporal graph neural networks (…
TASAC: a twin-actor reinforcement learning framework with stochastic policy for batch process control
Tanuja Joshi, Hariprasad Kodamana, Harikumar Kandath +1
Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting pl…
Twin actor twin delayed deep deterministic policy gradient (TATD3) learning for batch process control
Tanuja Joshi, Shikhar Makker, Hariprasad Kodamana +1
Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch. It is expected that…
Scalable Gaussian Processes for Predicting the Properties of Inorganic Glasses with Large Datasets
Suresh Bishnoi, R. Ravinder, Hargun Singh +2
Gaussian process regression (GPR) is a useful technique to predict composition--property relationships in glasses as the method inherently provides the standard deviation of the pr…
Deep Learning Aided Rational Design of Oxide Glasses
R. Ravinder, Karthikeya H. Sreedhara, Suresh Bishnoi +5
Despite the extensive usage of oxide glasses for a few millennia, the composition-property relationships in these materials still remain poorly understood. While empirical and phys…
Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning
Suresh Bishnoi, Sourabh Singh, R. Ravinder +4
Machine learning (ML) methods are becoming popular tools for the prediction and design of novel materials. In particular, neural network (NN) is a promising ML method, which can be…