Popular GitHub Repositories for Heat Exchanger

A scalable implementation of "Learning Structural Node Embeddings Via Diffusion Wavelets (KDD 2018)".

A Physics-Informed Neural Network to solve 2D steady-state heat equations.

Harness the power of deep reinforcement learning to optimize your Home Energy Management System (HEMS). Our tailored agent, trained on the CoSES ProHMo Modelica framework, efficiently controls a building's heat pump and thermal storage valve.

npviewer is an application that allows you to visualize the data in numpy's npy files in the form of heat maps. The application automatically selects the appropriate dimensions for display based on different modes.

This is a PINN based approach in solving high temperature heat transfer equations in manufacturing industries, with a focus on reducing the energy consumption and optimizing the sensor positioning.

Use a visual heat or motion map to count the number of people that enter and exit a store, factory, or warehouse aisle.

In this repository, monocular depth estimation is implemented using MiDaS v2.1 small model for Android mobile devices.

Use a visual heat or motion map to count the number of people that enter and exit a store, factory, or warehouse aisle.

A repository for solving heat equation in a rectangular fin with Physics Informed Neural Networks and Surrogate Models.

A Deep Multi-Modal Fusion Architecture for Heat Map-Based Object Detection using Segmentation

A review paper on PINNs.

Implementation of the AI model from Crysis with Heat Imaging and multi-object detection using Yolo.

:world_map: Interactive Live World News Heat Map with Natural Language Processing, Summaries, and Reddit Discussions :newspaper:

Solution of 1D and 2D PDEs using Physics Informed Neural Networks (PINNs)

A Physics-Informed Neural Network (PINN) framework for solving partial differential equations (PDEs) with FastAPI integration. This project implements PINNs for various physical systems including simple harmonic motion, heat transfer, wave propagation, and fluid dynamics. The framework provides a modular architecture for training.

Detecting bias in ML models using heat maps

Presentation of the paper "Data vs. Physics: The Apparent Pareto Front of Physics-Informed Neural Networks" by F. M. Rohrhofer, S. Posch, C. Gößnitzer and B. C. Geiger for the Simulation Intelligence course held at University of Trieste (UniTS).

? PINN Enterprise Platform - AI-Powered Physics Simulations with CopilotKit-style Research Canvas UI. Complete serverless architecture with RAG-powered code generation, 3D visualization, and global edge deployment.

? Create terminal plots easily with Granite, a lightweight Haskell library supporting scatter plots, histograms, bar charts, pie charts, box plots, line charts, and heat maps.

Set of applications suitable for heat treatment of steels learning