69 citations · 294 across the 28 of their papers we have counts for
7 papers · 1 filter
Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks
Suresh Bishnoi, Ravinder Bhattoo, Jayadeva +2
The time evolution of physical systems is described by differential equations, which depend on abstract quantities like energy and force. Traditionally, these quantities are derive…
Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics
Suresh Bishnoi, Jayadeva, Sayan Ranu +1
Neural networks (NNs) that exploit strong inductive biases based on physical laws and symmetries have shown remarkable success in learning the dynamics of physical systems directly…
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 (…
Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity
Samidha Verma, Burouj Armgaan, Sourav Medya +1
Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot pr…
GRAFENNE: Learning on Graphs with Heterogeneous and Dynamic Feature Sets
Shubham Gupta, Sahil Manchanda, Sayan Ranu +1
Graph neural networks (GNNs), in general, are built on the assumption of a static set of features characterizing each node in a graph. This assumption is often violated in practice…
GSHOT: Few-shot Generative Modeling of Labeled Graphs
Sahil Manchanda, Shubham Gupta, Sayan Ranu +1
Deep graph generative modeling has gained enormous attraction in recent years due to its impressive ability to directly learn the underlying hidden graph distribution. Despite thei…
StriderNET: A Graph Reinforcement Learning Approach to Optimize Atomic Structures on Rough Energy Landscapes
Vaibhav Bihani, Sahil Manchanda, Srikanth Sastry +2
Optimization of atomic structures presents a challenging problem, due to their highly rough and non-convex energy landscape, with wide applications in the fields of drug design, ma…