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Implementation of autoencoders in PyTorch
RBniCS - reduced order modelling in FEniCS (legacy)
In this repository, we develop use two machine learning algorithms (namely DT and KNN) to predict the POD coefficient of a static physical field for the objective of efficient prediction/reconstruc…
上海交通大学 Beamer 模版 | Beamer template for Shanghai Jiao Tong University
A differentiable PDE solving framework for machine learning
Caffe implementation of the paper "Deep Pyramidal Residual Networks" (https://arxiv.org/abs/1610.02915).
Pytorch implementation of FlowNet by Dosovitskiy et al.
Lightweight and scalable framework for Reinforcement Learning
Generative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields
machine learning-accelerated computational fluid dynamics
Turbulent viscosity field prediction using convolution neural networks
PyTorch implementation of the diffusion-based method for CFD data super-resolution proposed in the paper "A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction".
Shallow Learning for Flow Reconstruction with Limited Sensors and Limited Data
A Convolutional Variational Autoencoder (CVAE) for 3D CFD data reconstruction and generation.
A pytorch implementation of several approaches using PINN to slove turbulent flow
Multi-fidelity Generative Deep Learning Turbulent Flows
This codebase implements the system described in the paper: Dynamic Fluid Surface Reconstruction Using Deep Neural Network
GAROM-Generative Adversarial Reduce Order Modelling
Turbulent flow network source code
为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…
Latex code for making neural networks diagrams
This repository contains code used to create and train a deep neural network that replicates a RANS solver for aerodynamic prediction over airfoils.
A framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning