baratilab Goto Github PK

24789spring21

Course webpage for 24789: Deep Learning for Engineers (Spring 2021)

adaptive-grey-wolf-optimization-algorithm-agwo

adaptivesampling

airfoil-gcnn

Graph Convolutional Networks for Unstructured Flow Fields

auglichem

beadsight

Bead Sight Project Code

cafa

ForeCasting with Factorized Attention (CaFA), a computationally efficient Transformer-based weather prediction model

crazyconstruct

Additive Manufacturing for 3D printed blocks based on an LLM based input feed. Controlled through crazyflie API.

crazyswarm2-docker

Setup of Crazyflie Swarm using a Docker image for ROS 2

diffusion-based-fluid-super-resolution

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".

factformer

Official implementation of Scalable Transformer for PDE surrogate modelling

faultnet

fgn

Code repo for Fluid Graph Network

fluidgan-tensorflow

A TensorFlow framework for multi-physics phenomena (stationary, time-dependent) prediction using FluidGAN

gamd

Data and code for graph neural network accelerated molecular dynamics

glu3d-generative-lattice-units-with-3d-diffusion

graphene-rl

hagen-poiseuille-flow-with-lstm

Tutorial on using LSTM to predict Hagen-Poiseuille flow

llm_rag

Retrieval Augmented Generation with LLAMAINDEX

lstm-vae-for-dominant-motion-extraction

mab-os

Multi-Armed Bandits Optimizer Selection

manufacturingnet

This is the repository for the ManufacturingNet project.

meltpoolnet

MeltpoolNet: Melt pool Characteristic Prediction in Metal Additive Manufacturing Using Machine Learning

meshdqn

Using deep reinforcement learning for refining meshes in computational fluid dynamics

modulus

A PyTorch based deep-learning toolkit for developing DL models for physical systems

mst_pde

Multi-scale time-stepping of PDEs with Transformers

oformer

organoid_tracking

pca-her

pde-identification-features

Data-driven Identification of 2D Partial Differential Equations using Extracted Physical Features