Milad Ramezankhani

According to our database, Milad Ramezankhani authored at least 12 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

State of Health Estimation of Batteries Using a Time-Informed Dynamic Sequence-Inverted Transformer.

Janak M. Patel, Milad Ramezankhani, Anirudh Deodhar, Dagnachew Birru

CoRR, July, 2025

GITO: Graph-Informed Transformer Operator for Learning Complex Partial Differential Equations.

Milad Ramezankhani, Janak M. Patel, Anirudh Deodhar, Dagnachew Birru

CoRR, June, 2025

Accelerated Gradient-based Design Optimization Via Differentiable Physics-Informed Neural Operator: A Composites Autoclave Processing Case Study.

Janak M. Patel, Milad Ramezankhani, Anirudh Deodhar, Dagnachew Birru

CoRR, February, 2025

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, Dagnachew Birru

Eng. Appl. Artif. Intell., 2025

2024

Smart manufacturing under limited and heterogeneous data: a sim-to-real transfer learning with convolutional variational autoencoder in thermoforming.

Milad Ramezankhani, Mehrtash Harandi, Rudolf J. Seethaler, Abbas S. Milani

Int. J. Comput. Integr. Manuf., February, 2024

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, Dagnachew Birru

CoRR, 2024

2023

Non data hungry smart composite manufacturing using active transfer learning with sigma point sampling (SPSATL).

Debangsha Sarkar, Milad Ramezankhani, Apurva Narayan, Abbas S. Milani

Comput. Ind., October, 2023

A Sequential Meta-Transfer (SMT) Learning to Combat Complexities of Physics-Informed Neural Networks: Application to Composites Autoclave Processing.

Milad Ramezankhani, Abbas S. Milani

CoRR, 2023

2022

Investigation of a Sparse Autoencoder-Based Feature Transfer Learning Framework for Hydrogen Monitoring Using Microfluidic Olfaction Detectors.

Hamed Mirzaei, Milad Ramezankhani, Emily Earl, Nishat Tasnim, Abbas S. Milani, Mina Hoorfar

Sensors, 2022

A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study.

Milad Ramezankhani, Amir Nazemi, Apurva Narayan, Heinz Voggenreiter, Mehrtash Harandi, Rudolf J. Seethaler, Abbas S. Milani

Proceedings of the 5th IEEE International Conference on Industrial Cyber-Physical Systems, 2022

2021

An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing.

Milad Ramezankhani, Apurva Narayan, Rudolf J. Seethaler, Abbas S. Milani

Proceedings of the 4th IEEE International Conference on Industrial Cyber-Physical Systems, 2021

Toward Using Few-Shot Learning for Prediction of Complex In-Service Defects of Composite Products: A case Study.

Mohamad Khajezade, Milad Ramezankhani, Fatemeh Hendijani Fard, Mohamed S. Shehata, Abbas S. Milani

Proceedings of the 34th IEEE Canadian Conference on Electrical and Computer Engineering, 2021