Non-intrusive reduced-order modeling with geometry-informed snapshots. Current based registration is applied to compute the diffeomorphism between snapshots.
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Updated
Jun 17, 2024 - Python
Non-intrusive reduced-order modeling with geometry-informed snapshots. Current based registration is applied to compute the diffeomorphism between snapshots.
Data-driven reduced order modeling for nonlinear dynamical systems
Creating animation sequences between sparse key frames using motion phase features.
"Welcome to my Java Selenium Test Automation Framework! Developed from scratch, this framework utilizes TestNG, Maven, Apache POI, and Excel Data Reader. With a robust Page Object Model implementation, it offers seamless test case management and maintenance. Experience the benefits of TestNG, including parallel execution and flexible reporting.
This repository is a supplementary documentation for the Multi-Model Parameterized Koopman (MMPK) framework capturing results through software and hardware deployments
Delay Embedded Regressive Reduced Order Model
This repository is for Jarvis Bulldog Team of FacutyHack@Gateways23 with SGX3.
Fourier Feature Character Animation Controller Demo
Tutorials on math epidemiology and epidemiology informed deep learning methods
Machine learning project which studies the complex dynamics of earthquakes through spatial-temporal data analysis.
IDRLnet, a Python toolbox for modeling and solving problems through Physics-Informed Neural Network (PINN) systematically.
Statistical Analysis of Socioeconomic Data for Business insights
A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives
Supplementary materials to the lecture data driven audio signal processing
A novel neural network for effective learning of highly impulsive/oscillatory dynamic systems by jointly utilizing low-order derivatives
DataKick is a data analysis project that focuses on analyzing Premier League football data. We use machine learning algorithms and statistical models to uncover insights and trends that can help teams and coaches make better decisions.
A black box data driven model that considers the characterization and prediction of heat load in buildings connected to District Heating by using smart heat meters
COMMIT-NILM: COMputational MonItoring Tool for NILM Algorithms
Deep neural networks have garnered tremendous excitement in recent years thanks to their superior learning capacity in the presence of abundant data resources. However, collecting an exhaustive dataset covering all possible scenarios is often slow, expensive, and even impractical. The goal of this project is to devise a new learning framework th…
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