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Name: Koteswara Rao Putta
Type: User
Company: NTNU
Bio: I am a post-doctoral researcher at NTNU.
Location: Norway
Blog: https://www.linkedin.com/in/koteswara-rao-putta-345b4712/
Name: Koteswara Rao Putta
Type: User
Company: NTNU
Bio: I am a post-doctoral researcher at NTNU.
Location: Norway
Blog: https://www.linkedin.com/in/koteswara-rao-putta-345b4712/
Slides for CMU 10601, 10605
Work done for the course 'Mathematical Modelling of Chemical Engineering Processes', all of the work is in MATLAB
100 Days of ML Coding
My homework solutions for CMU Machine Learning Course (10-601 2018Fall)
Machine learning contest - October 2016 TLE
Purdue University workshop, March 2019
This repository holds data analysis of the hydrogen required by the UI fleet and MTD fleet to become carbon free.
This project used the forward and backward difference schemes for coding the solution of partial differential equations like linear and non linear convection, diffusion, burger's equation, laplace and navier-stokes equation to simulate the physical phenomena depicted by them.
The objective of the project was to develop a predictive model that combines various econometric measures to foresee a financial condition (Bankruptcy or not) of a firm based on a financial balance sheet data set available on Kaggle. This was done as part of the Data Mining course at the Krannert Business Analytics Program, Purdue University - Team: Zaid Ahmed, Mohinder Goyal and Maharshi Dutta
All the adsorption process related
Advanced Data Analytics for Chemical Engineers
Projects from the 252-0535-00L Advanced Machine Learning at ETH Zürich Fall Semester 2019
Endothermic Reactor Modeling with Matlab
Various models used to train a machine learning model to predict viable metal-hydride based hydrogen compressors.
Code I wrote which simulates an ammonia synthesis reactor by solving ODE system for flow, shell&tube heat transfer, pressure, and chemical kinetics
Effectiveness factor in progress
Repository containing Jupyter notebooks for the course: An Introduction to Machine Intelligence for Architects and other Nonengineers ETH Zurich, Chair for CAAD, Autumn semester 2019
BMI Hydraulics Project on Anaerobic Digester
The purpose of this app is to optimize the model parameters in order to increase the profitability of the bioreactor. The user will be able to adjust initial bacteria concentrations, reactor temperature (both static and dynamic), etc., and see a visual representation of the outcome. Note that not all adjustable parameters will have an effect on every plot. As soon as the user loads the app, the model is compiled and run using the current parameter settings. As the user adjusts these parameters, the plots and tables will update according to the new settings once the "Re-simulate" button has been clicked (This button is on each of the table and plot tabs). The tabs on the left then allow the user to navigate the simulated reactor output and read more about the process. You can also specify the time in which to truncate the data and evaluate the output, though the minimum value is currently 100 h. The reactor that follows the anaerobic digestor is capable of removing small quantities left over. Ideally you would set a minimum acceptable concentration, and optimize parameters to decrease the time required to reduce the contaminants to that concentration, though this not been implemented due to the lack of observational data.
Full stack Java Application that cuts costs of creation, invoice management and process simulation associated with setting up pilot water purification plants.
Applied Machine Learning @ http://amitkaps.com/ml
A set of utility functions and Jupyter notebooks for attainable region (AR) computations.
Artificial Intelligence By Example, published by Packt
Kaggle competition of ASHRAE_Energy_Predictor, use brillent machine learning models and won 8th from 3595 teams
Projects on Aspen Plus and AspenHYSYS
Optimization of a chemical reactor using Aspen Plus, python and the NSGA2 algorithm
A browser based interface for the Open Source Energy Modelling System model (OSeMOSYS), the Model Management Infrastructure (MoManI) provides tools to construct energy models, explore scenarios and visualize results.
Automated feature engineering in Python with Featuretools
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.