13 citations · 25 across the 16 of their papers we have counts for
18 papers
Money-Mule Detection as a Relational Learning Problem: A Study on the RBIH NFPC Dataset
Owais Ahmad, Anirudh Deodhar
Money-mule networks move criminal funds through many small transfers, each of which looks ordinary on its own. What marks an account as a mule lies outside any single transfer: in…
From Metaheuristics to Exact Methods: A CP-SAT Approach for Multi-Objective Healthcare Workforce Scheduling
Vipul Patel, Anirudh Deodhar, Dagnachew Birru
Healthcare workforce scheduling is an NP-hard optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, employee preferences and cost ob…
CP-WSP: A Declarative CP-SAT Framework for Configurable Multi-Constraint Workforce Scheduling
Vipul Patel, Anirudh Deodhar, Dagnachew Birru
Workforce scheduling is an NP-hard combinatorial optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, employee preferences and oper…
GNBAN: Graph Neural Basis Attention Networks for Long-Horizon Forecasting over Large Entity Sets
Janak M. Patel, Anirudh Deodhar, Dagnachew Birru
Demand forecasting at the bottom of a retail hierarchy requires predicting tens of thousands of correlated long-horizon series across products, stores, and regions. Modern systems…
Intelligent Human-Machine Partnership for Manufacturing: Enhancing Warehouse Planning through Simulation-Driven Knowledge Graphs and LLM Collaboration
Himabindu Thogaru, Saisubramaniam Gopalakrishnan, Zishan Ahmad +1
Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in mod…
A Multi-Objective Genetic Algorithm for Healthcare Workforce Scheduling
Vipul Patel, Anirudh Deodhar, Dagnachew Birru
Workforce scheduling in the healthcare sector is a significant operational challenge, characterized by fluctuating patient loads, diverse clinical skills, and the critical need to…
DETNO: A Diffusion-Enhanced Transformer Neural Operator for Long-Term Traffic Forecasting
Owais Ahmad, Milad Ramezankhani, Anirudh Deodhar
Accurate long-term traffic forecasting remains a critical challenge in intelligent transportation systems, particularly when predicting high-frequency traffic phenomena such as sho…
Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad +1
Analyzing large, complex output datasets from Discrete Event Simulations (DES) of warehouse operations to identify bottlenecks and inefficiencies is a critical yet challenging task…
State of Health Estimation of Batteries Using a Time-Informed Dynamic Sequence-Inverted Transformer
Janak M. Patel, Milad Ramezankhani, Anirudh Deodhar +1
The rapid adoption of battery-powered vehicles and energy storage systems over the past decade has made battery health monitoring increasingly critical. Batteries play a central ro…
GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations
Milad Ramezankhani, Janak M. Patel, Anirudh Deodhar +1
We present a novel graph-informed transformer operator (GITO) architecture for learning complex partial differential equation systems defined on irregular geometries and non-unifor…
Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study
Janak M. Patel, Milad Ramezankhani, Anirudh Deodhar +1
Simulation and optimization are crucial for advancing the engineering design of complex systems and processes. Traditional optimization methods require substantial computational ti…
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition
Milad Ramezankhani, Rishi Yash Parekh, Anirudh Deodhar +1
Deep operator networks (DeepONet) and neural operators have gained significant attention for their ability to map infinite-dimensional function spaces and perform zero-shot super-r…
An Advanced Physics-Informed Neural Operator for Comprehensive Design Optimization of Highly-Nonlinear Systems: An Aerospace Composites Processing Case Study
Milad Ramezankhani, Anirudh Deodhar, Rishi Yash Parekh +1
Deep Operator Networks (DeepONets) and their physics-informed variants have shown significant promise in learning mappings between function spaces of partial differential equations…
HyperLoRA for PDEs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed neural networks (PINNs) have been widely used to develop neural surrogates for solutions of Partial Differential Equations. A drawback of PINNs is that they have t…
Symbolic Regression for PDEs using Pruned Differentiable Programs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
Physics-informed Neural Networks (PINNs) have been widely used to obtain accurate neural surrogates for a system of Partial Differential Equations (PDE). One of the major limitatio…
Real-time Health Monitoring of Heat Exchangers using Hypernetworks and PINNs
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
We demonstrate a Physics-informed Neural Network (PINN) based model for real-time health monitoring of a heat exchanger, that plays a critical role in improving energy efficiency o…
Physics Informed Symbolic Networks
Ritam Majumdar, Vishal Jadhav, Anirudh Deodhar +3
We introduce Physics Informed Symbolic Networks (PISN) which utilize physics-informed loss to obtain a symbolic solution for a system of Partial Differential Equations (PDE). Given…
Semi-Supervised Cascaded Clustering for Classification of Noisy Label Data
Ashit Gupta, Anirudh Deodhar, Tathagata Mukherjee +1
The performance of supervised classification techniques often deteriorates when the data has noisy labels. Even the semi-supervised classification approaches have largely focused o…