13 citations · 29 across the 7 of their papers we have counts for
8 papers
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…
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…
A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing
Milad Ramezankhani, Abbas S. Milani
Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural…
A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study
Milad Ramezankhani, Amir Nazemi, Apurva Narayan +4
Despite the successful implementations of physics-informed neural networks in different scientific domains, it has been shown that for complex nonlinear systems, achieving an accur…