Pinned Repositories
ATL
An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing
BNN-HMC
CRN
Data-driven-MFPINNs
This repository contains the code and data accompanying the paper entitled "A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study"
github-pages-with-jekyll
HMC-PINNs
This repository presents a JAX implementation of BPINN model developed for curing composite materials. See below for more details: https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0432643
MCDM_DOE_UBC_ENGR_589
Python codes for the course Multicriteria Decision-Making and Design of Experiments.
SAMPE2023_Tutorial
The slides and the Python code of the tutorial entitled "When Data-efficient Machine Learning Comes to the Rescue: An AI-based Optimization Framework for Advanced Manufacturing" presented as part of SAMPE 2023 conference in Seattle, WA.
SequentialMetaTransferPINNs
This repository presents a JAX implementation of the paper entitled "Meta-Transfer Sequential Learning of Physics-Informed Neural Networks in Advanced Composites Manufacturing". The proposed framework integrates a sequential learning strategy with the meta-transfer learning approach to make the training of PINNs in highly nonlinear systems
Vibration-PINNs
This repository presents a series of analysis on the performance of Physics-Informed Neural Networks in vibrational systems. The limitation of PINNs in learning highly nonlinear systems with long temporal domains is discussed and the potential solutions are investigated.
miladramzy's Repositories
miladramzy/Data-driven-MFPINNs
This repository contains the code and data accompanying the paper entitled "A Data-driven Multi-fidelity Physics-informed Learning Framework for Smart Manufacturing: A Composites Processing Case Study"
miladramzy/SequentialMetaTransferPINNs
This repository presents a JAX implementation of the paper entitled "Meta-Transfer Sequential Learning of Physics-Informed Neural Networks in Advanced Composites Manufacturing". The proposed framework integrates a sequential learning strategy with the meta-transfer learning approach to make the training of PINNs in highly nonlinear systems
miladramzy/Vibration-PINNs
This repository presents a series of analysis on the performance of Physics-Informed Neural Networks in vibrational systems. The limitation of PINNs in learning highly nonlinear systems with long temporal domains is discussed and the potential solutions are investigated.
miladramzy/HMC-PINNs
This repository presents a JAX implementation of BPINN model developed for curing composite materials. See below for more details: https://open.library.ubc.ca/soa/cIRcle/collections/ubctheses/24/items/1.0432643
miladramzy/SAMPE2023_Tutorial
The slides and the Python code of the tutorial entitled "When Data-efficient Machine Learning Comes to the Rescue: An AI-based Optimization Framework for Advanced Manufacturing" presented as part of SAMPE 2023 conference in Seattle, WA.
miladramzy/ATL
An Active Transfer Learning (ATL) Framework for Smart Manufacturing with Limited Data: Case Study on Material Transfer in Composites Processing
miladramzy/BNN-HMC
miladramzy/CRN
miladramzy/github-pages-with-jekyll
miladramzy/MCDM_DOE_UBC_ENGR_589
Python codes for the course Multicriteria Decision-Making and Design of Experiments.
miladramzy/meta-transfer-learning
TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)
miladramzy/miladramzy.github.io
A beautiful, simple, clean, and responsive Jekyll theme for academics
miladramzy/opendata
SkillCorner Open Data with 9 matches of broadcast tracking data.