Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

We introduce a novel approach for parameter generation, named neural network parameter diffusion (p-diff), which employs a standard latent diffusion model to synthesize a new set of parameters

ControlLoRA: A Lightweight Neural Network To Control Stable Diffusion Spatial Information

Diffusion Convolutional Recurrent Neural Network Implementation in PyTorch

A small neural network to provide interoperability between the latents generated by the different Stable Diffusion models.

Upscaling stable diffusion latents using a small neural network.

ControlLoRA Version 2: A Lightweight Neural Network To Control Stable Diffusion Spatial Information Version 2

GREAD: Graph Neural Reaction-Diffusion Networks, ICML 2023

PyTorch implementation of Diffusion Convolutional Recurrent Neural Network

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-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions

[Neural Networks] RGDAN: A random graph diffusion attention network for traffic prediction

PyTorch code for Diffusion Mechanism in Neural Network: Theory and Applications

An implementation of Diffusion-Convolutional Neural Networks in Lasagne and Theano.

For this project, the Stable Diffusion neural network model was used to generate images conditioned on QR code inputs via ControlNet.

Convolutional-recurrent neural networks approximate diffusion tractography from T1-weighted MRI and associated anatomical context

[IEEE ICCBD+AI 2025] Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks

Topological Recurrent Neural Network for Diffusion Prediction

Physics Informed Neural Networks (based on Raissi et al) extended to three dimensions on the heat diffusion equations

Code for reproducing models in the paper "Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI"

Reaction diffusion system prediction based on convolutional neural network

[ICMLW 2023, IEEE CISS 2024] Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy

ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion

The source code of "Deep attention diffusion graph neural networks for text classification"

PyTorch-Geometric Implementation of MarkovGNN method published in Graph Learning@WWW 2022 titled "MarkovGNN: Graph Neural Networks on Markov Diffusion"

with comprehensive numerical study on solving neutron diffusion eigenvalue problems) We present a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In order to achieve an engineering acceptable accuracy for complex engineering problems, a very small amount of prior data from physical experiments are suggested to be used, to improve the accuracy and efficiency of training. We design an adaptive optimization procedure with Adam and LBFGS to accelerate the convergence in the training stage. We discuss the effect of different physical parameters, sampling techniques, loss function allocation and the generalization performance of the proposed DEPINN model for solving complex problem. The feasibility of proposed DEPINN model is tested on three typical benchmark problems, from simple geometry to complex geometry, and from mono-energetic equation to two-group equations. Numerous numerical results show that DEPINN can efficiently solve NDEPs with an appropriate optimization procedure. The proposed DEPINN can be generalized for other input parameter settings once its structure been trained. This work confirms the possibility of DEPINN for practical engineering applications in nuclear reactor physics.

A reference implementation of “Information Diffusion Prediction with Graph Neural Ordinary Differential Equation Network”

DiffusionBlocks: Block-wise Neural Network Training via Diffusion Interpretation

Official implementation of "Relational Data Generation with Graph Neural Networks and Latent Diffusion Models"

A Light Neural Network To Control Stable Diffusion Spatial Information tuned by Chinese

Repository for "Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective"