Deep Unfolding Network for Image Super-Resolution (CVPR, 2020) (PyTorch)
Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis (Machine Intelligence Research 2023)
Camera Lens Super-Resolution in CVPR 2019
[WACV 2024 Oral] - ARNIQA: Learning Distortion Manifold for Image Quality Assessment
DIAR software for synthetic document image and groundtruth generation, with various degradation models for data augmentation
[WACV 2024] - Reference-based Restoration of Digitized Analog Videotapes
Implementation of Pontryagin's Minimum Principle for microgrid energy storage control
This repository presents a Physics-Informed Deep Learning framework for Remaining Useful Life (RUL) prediction of rolling element bearings using vibration signal analysis and advanced representation learning techniques.
A physics-grounded, agent-driven digital twin for HP Metal Jet S100 3D printer
Simulation of Drug Delivery Diffusion Processes in Polymer-Based Nanopharmaceuticals considering Matrix Degradation using FEniCS Legacy.
this project implements physics-informed machine learning models to predict lithium-ion battery degradation and performance metrics, including RUL, SoC, and SoH.
Modular lithium-ion battery model including SOC/OCV modelling, CC/CV charging, cycle based degradation, calendar ageing, Arrhenius based capacity degradation and temperature dynamics modelling.
Final Project for CS 230: Deep Learning. Project title: Deep Learning Approaches to State-of-Health Estimation
Reproducible Python workflow for the LEO thermo-mechanico-electrical degradation model.
LSTM/RNN models for battery State-of-Health (SOH) prediction and degradation forecasting on industrial time-series data
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