Name: Vigneashwara Pandiyan
Type: User
Company: ETH Swiss Federal Laboratories for Materials Science and Technology (EMPA)
Bio: Postdoctoral Researcher at Laboratory for Advanced Materials Processing, ETH Swiss Federal Laboratories for Materials Science and Technology (EMPA).
Twitter: vigneashpandiya
Location: Switzerland
Blog: https://www.linkedin.com/in/vigneashpandiyan/
Vigneashwara Pandiyan's Projects
A project to solve and map heat diffusion equation with the FDM
Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions
In Situ Quality Monitoring in Direct Energy Deposition Process using Co-axial Process Zone Imaging and Deep Contrastive Learning
Monitoring of direct energy deposition process using deep-net based manifold learning and co-axial melt pool imaging
Monitoring Of Laser Powder Bed Fusion Process By Bridging Dissimilar Process Maps Using Deep Learning-based Domain Adaptation on Acoustic Emissions
Repositry supporting two publications on LPBF process monitoring using acoustic emissions
Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process
Self-Supervised Bayesian Representation Learning of Acoustic Emissions from Laser Powder Bed Fusion Process for In-situ Monitoring
Real-Time Monitoring and Quality Assurance for Laser-Based Directed Energy Deposition: Integrating Coaxial Imaging and Self-Supervised Deep Learning Framework
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
Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process
Deep learning-based monitoring of laser powder bed fusion process on variable time-scales using heterogeneous sensing and operando X-ray radiography guidance
autoformer unofficial reproduction
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Computational examples for lectures on signal processing of Acoustical Engineering
PyTorch code for CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting (ICLR 2022)
NMA deep learning course
List of useful data augmentation resources. You will find here some not common techniques, libraries, links to github repos, papers and others.
Koopman operator: learning dynamical systems | Diffusion Maps: Describing geometry in point clouds.
Deterministic Decoding for Discrete Data in Variational Autoencoders
Extraction of mechanical properties of materials through deep learning from instrumented indentation
Deep Learning with PyTorch, published by Packt
PyTorch Implementation of Lusch et al DeepKoopman
Experiments for understanding disentanglement in VAE latent representations
Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)
PyTorch implementation of "Disentangling by Factorising" (https://arxiv.org/pdf/1802.05983.pdf)
Implementing a Gaussian Process regression model from scratch