I love learning
kapusuzoglu Goto Github PK
Name: Berkcan Kapusuzoglu
Type: User
Company: Capital One
Bio: PhD @ Vanderbilt University
Name: Berkcan Kapusuzoglu
Type: User
Company: Capital One
Bio: PhD @ Vanderbilt University
Bayesian Neural Network
website related stuff
A Convolution Neural Network (CNN) Tutorial
deeplearning LSTM adjusted
The digital twin paradigm that integrates the information obtained from sensor data, physics models, as well as operational and inspection/maintenance/repair history of a system (or a component) of interest, can potentially be used to optimize operational parameters of the system in order to achieve a desired performance or reliability goal. In this article, we develop a methodology for intelligent mission planning using the digital twin approach, with the objective of performing the required work while meeting the damage tolerance requirement. The proposed approach has three components: damage diagnosis, damage prognosis, and mission optimization. All three components are affected by uncertainty regarding system properties, operational parameters, loading and environment, as well as uncertainties in sensor data and prediction models. Therefore the proposed methodology includes the quantification of the uncertainty in diagnosis, prognosis, and optimization, considering both aleatory and epistemic uncertainty sources. We discuss an illustrative fatigue crack growth experiment to demonstrate the methodology for a simple mechanical component, and build a digital twin for the component. Using a laboratory experiment that utilizes the digital twin, we show how the trio of probabilistic diagnosis, prognosis, and mission planning can be used in conjunction with the digital twin of the component of interest to optimize the crack growth over single or multiple missions of fatigue loading, thus optimizing the interval between successive inspection, maintenance, and repair actions.
Highlights • Physics-informed machine learning is investigated for global sensitivity analysis. • Physics and test data are fused to maximize the accuracy of sensitivity estimates. • Uncertainties in Gaussian process and deep neural network models are included. • Accuracy, uncertainty and computational effort of proposed approaches are compared.
root repo
my blog
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
Joel Greenblatt - Magic Formula Investing
Replicated the architecture of Llama to create miniLlama for testing / demo purposes.
Multi-objective optimization
Contributions
Principal Component Analysis (PCA)
What can machine learning tell you about your weight?
Physics-Informed and Hybrid Machine Learning in Additive Manufacturing: Application to Fused Filament Fabrication
This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabrication (FFF). A one-dimensional heat transfer analysis providing an estimate of the temperature profile of the filaments is coupled with a sintering neck growth model to assess the bond quality that occurs at the interfaces between adjacent filaments. Predicting the variability in the FFF process is essential for achieving proactive quality control of the manufactured part; however, the models used to predict the variability are affected by assumptions and approximations. This paper systematically quantifies the uncertainty in the bond quality model prediction due to various sources of uncertainty, both aleatory and epistemic, and includes the uncertainty in the process parameter optimization. Variance-based sensitivity analysis based on Sobol' indices is used to quantify the relative contributions of the different uncertainty sources to the uncertainty in the bond quality. A Gaussian process (GP) surrogate model is constructed to compute and include the model error within the optimization. Physical experiments are conducted to show that the proposed formulation for process parameter optimization under uncertainty results in high bond quality between adjoining filaments of the FFF product.
Save plots as PDF/PGF (Progressive Graphics File) for direct upload to LaTeX.
Umami is a simple, fast, privacy-focused alternative to Google Analytics.
This document is prepared based on the lectures and notes for the graduate level course- ‘Uncertainty Quantification’ (CE 6310) taught by Dr. Sankaran Mahadevan at Vanderbilt University. Course materials of ‘Spring 2015’ and ‘Spring 2019’.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.