vigneashpandiyan Goto Github PK

2dheateqnfdm

A project to solve and map heat diffusion equation with the FDM

additive-manufacturing-acoustics-semisupervised-learning

Semi-supervised monitoring of laser powder bed fusion process based on acoustic emissions

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

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-feature-engineering-acoustic-emission

Repositry supporting two publications on LPBF process monitoring using acoustic emissions

additive-manufacturing-multi-material-composition-monitoring-using-sensor-fusion

Qualify-As-You-Go Sensor Fusion, Process Zone Signatures and Deep Contrastive Learning for Multi-Material Composition Monitoring in LPBF Process

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

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-transfer-learning

Deep transfer learning of additive manufacturing mechanisms across materials in metal-based laser powder bed fusion process

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

autoformer_pytorch

autoformer unofficial reproduction

ca-tcc

Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification

codes_proc_de_sinais

Computational examples for lectures on signal processing of Acoustical Engineering

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

data-augmentation-review

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to github repos, papers and others.

datafold

Koopman operator: learning dynamical systems | Diffusion Maps: Describing geometry in point clouds.

dd-vae

Deterministic Decoding for Discrete Data in Variational Autoencoders

deep-learning-for-indentation

Extraction of mechanical properties of materials through deep learning from instrumented indentation

deep-learning-koopango-ips

deep-learning-with-pytorch

Deep Learning with PyTorch, published by Packt

deepkoopmanlusch

PyTorch Implementation of Lusch et al DeepKoopman

deterministic-autoencoders

diffusion_maps

disentangling-vae

Experiments for understanding disentanglement in VAE latent representations

factorvae

Pytorch implementation of FactorVAE proposed in Disentangling by Factorising(http://arxiv.org/abs/1802.05983)

factorvae-1

PyTorch implementation of "Disentangling by Factorising" (https://arxiv.org/pdf/1802.05983.pdf)

gaussian-process

Implementing a Gaussian Process regression model from scratch