Stars
🚀 EvoAgentX: Building a Self-Evolving Ecosystem of AI Agents
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
The missing star history graph of GitHub repos - https://star-history.com
[CVPR 2025 Oral] Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models
Qwen-Image is a powerful image generation foundation model capable of complex text rendering and precise image editing.
PaddleCFD is a deep learning toolkit for surrogate modeling, equation discovery, shape optimization and flow-control strategy discovery in the field of fluid mechanics.
PaddleMaterials is a data-mechanism dual-driven, foundation model development and deployment, end to end toolkit based on PaddlePaddle deep learning framework for materials science and engineering.
PaddleScience is SDK and library for developing AI-driven scientific computing applications based on PaddlePaddle.
Code for our paper "Learning of Mechanical System Dynamics with Dissipative Lagrangian Neural Networks"
In this repository, you will find the different python scripts to train the available models on the AirfRANS dataset proposed at the NeurIPS 2022 Datasets and Benchmarks Track conference.
To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffus…
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
A Python implementation of global optimization with gaussian processes.
PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
Bayesian optimized physics-informed neural network for parameter estimation
Pytorch implementation of Bayesian physics-informed neural networks
Easily convert OpenFOAM cases into dataframes for machine learning
Learning in infinite dimension with neural operators.
[ICML 2024] Official Pytorch implementation of the paper "A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data"
DAFoam: Discrete Adjoint with OpenFOAM for High-fidelity Multidisciplinary Design Optimization