Vigneashwara Pandiyan's repositories
Additive-Manufacturing-Transfer-Learning
Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process
Additive-Manufacturing-DED-Contrastive-Learners
In Situ Quality Monitoring in Direct Energy Deposition Process using Co-axial Process Zone Imaging and Deep Contrastive Learning
Additive-Manufacturing-DED-Manifold-Learning
Monitoring of direct energy deposition process using deep-net based manifold learning and co-axial melt pool imaging
Tribology-LSTM-Encoder_Decoder
Long short-term memory based semi-supervised encoder-decoder for early prediction of failures in self-lubricating bearings
Additive-Manufacturing-Self-Supervised-Learning-Coaxial-DED-Process-Zone-Imaging
Real-Time Monitoring and Quality Assurance for Laser-Based Directed Energy Deposition: Integrating Coaxial Imaging and Self-Supervised Deep Learning Framework
Additive-Manufacturing-Sensor-Selection-Acoustic-Emission
Sensor selection for process monitoring based on deciphering acoustic emissions from different dynamics of the Laser Powder Bed Fusion process using Empirical Mode Decompositions and Interpretable Machine Learning
Additive-Manufacturing-Variable-Time-Scales
Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance
PyTorch-PDQN-for-Digital-Twin-ACS
PyTorch implementation of RIC for conveyor systems with Deep Q-Networks (DQN) and Profit-Sharing (PS). Wang, T., Cheng, J., Yang, Y., Esposito, C., Snoussi, H., & Tao, F. (2020). Adaptive Optimization Method in Digital Twin Conveyor Systems via Range-Inspection Control. IEEE Transactions on Automation Science and Engineering.
Additive-Manufacturing-Acoustics-Semisupervised-Learning
Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions
Additive-Manufacturing-Feature-Engineering-Acoustic-Emission
Repositry supporting two publications on LPBF process monitoring using acoustic emissions
Additive-Manufacturing-Domain-adaptation-for-Bridging-Dissimilar-Process-Maps-Acoustic-Emission
Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions
Additive-Manufacturing-Self-Supervised-Bayesian-Representation-Learning-Acoustic-Emission
Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
autoformer_pytorch
autoformer unofficial reproduction
CA-TCC
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
CoST
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
course-content-dl
NMA deep learning course
datafold
Koopman operator: learning dynamical systems | Diffusion Maps: Describing geometry in point clouds.
deep-learning-for-indentation
Extraction of mechanical properties of materials through deep learning from instrumented indentation
DeepKoopmanLusch
PyTorch Implementation of Lusch et al DeepKoopman
Machine-Learning-in-Materials-Science
The materials for the Fall ML in Materials course at the Tickle College of Engineering at the University of Tennessee at the University of Tennessee, Knoxville
Neural-Koopman-Lyapunov-Control
Neural Koopman Lyapunov Control
Neural_Koopman_pooling
[CVPR 2023] Neural Koopman Pooling: Control-Inspired Temporal Dynamics Encoding for Skeleton-Based Action Recognition
PyTorch-VAE
A Collection of Variational Autoencoders (VAE) in PyTorch.
scRAE
Code for scRAE: Deterministic Regularized Autoencoders with Flexible Priors for Clustering Single-cell Gene Expression Data
Tribology-Classification-of-wear-in-human-joints
Classification of progressive wear on a multi-directional pin-on-disc tribometer simulating conditions in human joints - UHMWPE against CoCrMo using Acoustic Emission and Machine Learning
udkm1Dsim
A Python Simulation Toolkit for 1D Ultrafast Dynamics in Condensed Matter