AlirezaSamari / physics-informed-heat-equationLinks
Efficiently solve the 2D heat equation using a Physics-Informed Neural Network (PINN). Simulate and predict temperature distributions with machine learning and physics-based constraints. Ideal for educational exploration and practical applications.
☆12Updated last year
Alternatives and similar repositories for physics-informed-heat-equation
Users that are interested in physics-informed-heat-equation are comparing it to the libraries listed below
Sorting:
- In recent years, the use of physics-informed neural networks (PINNs) has gained popularity across several engineering disciplines due to …☆11Updated 2 years ago
- This repository contains the Python code for the paper "Transfer learning-based PINN model of 3D temperature field prediction for blue la…☆22Updated last year
- The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks☆39Updated 3 years ago
- We introduce an innovative physics-informed LSTM framework for metamodeling of nonlinear structural systems with scarce data.☆97Updated 2 years ago
- Physics Informed Neural Network for solving 3D transient heat transfer☆14Updated last year
- ☆90Updated 2 years ago
- Enhancing PINNs for Solving PDEs via Adaptive Collocation Point Movement and Adaptive Loss Weighting☆38Updated 2 years ago
- The digital twin paradigm that integrates the information obtained from sensor data, physics models, as well as operational and inspectio…☆18Updated 3 years ago
- This is a PINN based approach in solving high temperature heat transfer equations in manufacturing industries, with a focus on reducing t…☆21Updated last year
- PINN (Physics-Informed Neural Networks) for the 2D heat equation☆15Updated last week
- Physics-Informed Neural Network SurrogaTe for Rapidly Identifying Parameters in Energy Systems☆46Updated last year
- Research/development of physics-informed neural networks for dynamic systems☆31Updated 11 months ago
- Physics-guided Convolutional Neural Network☆68Updated 5 years ago
- ☆131Updated 3 years ago
- Identification of Bouc–Wen type models using the transitional Markov chain Monte Carlo method☆14Updated 8 months ago
- Boosting the training of physics informed neural networks with transfer learning☆27Updated 4 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆28Updated 2 years ago
- Flow field reconstruction and prediction of the 2D cylinder flow using data-driven physics-informed neural network combined with long sho…☆39Updated last year
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- In his project, we proposed a new acquisition function for kriging-based reliability analysis, namely expected uncertainty reduction (EUR…☆10Updated 3 years ago
- 一个简易的模块化物理信息神经网络实现(PINN)☆49Updated this week
- Optimizing Physics-Informed NN using Multi-task Likelihood Loss Balance Algorithm and Adaptive Activation Function Algorithm☆32Updated 2 years ago
- Simulation of a SDOF Bouc-Wen-Baber-Noori hysteretic system☆13Updated 4 years ago
- An improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based …☆29Updated 2 years ago
- This is the code and data for the IEEE TPEL paper "Parameter Estimation of Power Electronic Converters with Physics-informed Machine Lear…☆69Updated last year
- This repo contains a PyTorch-based AE-ConvLSTM model for fluid flow prediction. It can forecast 5–10 time steps per forward pass and over…☆27Updated 5 months ago
- ☆21Updated 2 years ago
- Code used to generate the results of the paper: Nascimento et al. A framework for Li-ion battery prognosis based on hybrid Bayesian physi…☆58Updated 2 years ago
- 🌌 Applications of Physics-Informed ML: A collection of notebooks from my Masters research, exploring how machine learning can solve scie…☆11Updated last year
- Codes of our journal paper: Indirect identification of bridge frequencies using a four-wheel vehicle: Theory and three-dimensional simula…☆19Updated 2 months ago