12 citations · 12 across the 2 of their papers we have counts for
2 papers
cs.LG2023
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…
cs.CE2022★ 12 cited
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…