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This is a neural network approximating the two-dimensional signed distance functions of polygons.
Graph Convolutional Networks for Unstructured Flow Fields
An improved and generic PINNs for fluid dynamic analysis is proposed. This approach incorporates three key improvements: residual-based adaptive sampling, which automatically samples points in areas with larger residuals; adaptive loss weights, which balance the loss terms effectively; and the utilization of the DE optimization algorithm
POD-RNN and CRAN
A Framework for Remaining Useful Life Prediction Based on Self-Attention and Physics-Informed Neural Networks
Constitutive Artificial Neural Networks (CANNs) for modeling of hyperelastic materials
💧CFD(计算流体力学)资料汇总、学习笔记,欢迎补充...
Use ChatGPT to summarize the arXiv papers. 全流程加速科研,利用chatgpt进行论文总结+润色+审稿+审稿回复
Turbulent viscosity field prediction using convolution neural networks
This folder contains a sample code for the use of convolutional neural network for fluid force prediction of bluff body flows.
This repo contains some tutorial type programs showing some basic ways machine learning can be applied to CFD.
A list of papers relating Computational Physics and Machine Learning
We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.
This is the source code for our paper "Towards high-accuracy deep learning inference of compressible turbulent flows over aerofoils"
CVPR 2023 论文和开源项目合集
A framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning
Extraction of mechanical properties of materials through deep learning from instrumented indentation
Physics-guided neural network framework for elastic plates
Deep LSTM for highly nonlinear system modeling
Learning Continuous Signed Distance Functions for Shape Representation
Deep Learning of Vortex Induced Vibrations
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".
Physics-informed learning of governing equations from scarce data
codes for FinNet: Finite Difference Network for solving differential equations
Fluid simulation engine for computer graphics applications
Use Fourier transform to learn operators in differential equations.
[NeurIPS 2021] Galerkin Transformer: a linear attention without softmax
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.