zonexo

zonexo's Stars

• ChenguangZhang/sdfibm

  Immersed boundary method empowered by signed distance field, and OpenFOAM.

  Language:C++13551

• yuhao-yang-cy/a2physics

  a set of notes for CIE A-Level physics (9702)

  Language:TeX9317

• luohancfd/tecplotReaderLegacy

  python script to load tecplot binary file

  Language:Python51

• philippemiron/tecplot-binary-read

  Tecplot parser to read binary format (.plt)

  Language:C++1811

• awaldm/tecreader

  A python reader to time series data in Tecplot binary format

  Language:Python1

• FlowModelingControl/flowtorch

  flowTorch - a Python library for analysis and reduced-order modeling of fluid flows

  Language:Python12744

• A-Sam/DL-ROM

  Deep Learning for Reduced Order Modelling - HDM

  Language:Jupyter Notebook2

• polatory/polatory

  Fast radial basis function interpolation for large scale data

  Language:C++15319

• JamesMarshall31/design-of-experiments

  A Python Package for intuitive design of experiments with user-friendly analysis of results. The aim is for this package to rival the DOE capabilities of commercial software such as JMP. Currently designs and analysis will be more geared towards investigations following the Response Surface Methodology.

  Language:Python227

• mathLab/BladeX

  BladeX - Python Blade Morphing

  Language:Python6530

• MathEXLab/PySPOD

  A Python package for spectral proper orthogonal decomposition (SPOD).

  Language:Jupyter Notebook9929

• cwentland0/ae_rom_training

  Basic framework for training some simple autoencoder for reduced-order modeling.

  Language:Python3

• Romit-Maulik/CAE_LSTM_ROMS

  Supporting code for "Reduced-order modeling of advection-dominated systems with recurrent neural networks and convolutional autoencoders"

  Language:Jupyter Notebook1811

• erdc/aa_autoencoder_mca

  Supporting code for "reduced order modeling using advection-aware autoencoders"

  Language:Python61

• arashsaber/Deep-Convolutional-AutoEncoder

  This is a tutorial on creating a deep convolutional autoencoder with tensorflow.

  Language:Python5723

• Seratna/TensorFlow-Convolutional-AutoEncoder

  This is an implementation of Convolutional AutoEncoder using only TensorFlow

  Language:Python11657

• plasma-umass/scalene

  Scalene: a high-performance, high-precision CPU, GPU, and memory profiler for Python with AI-powered optimization proposals

  Language:Python11.5k384

• kazutotess/ML-ROM_Various_Shapes

  This repository contains the simple source codes of "Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes"

  Language:Python1510

• mjecke/pyfrontal

  A fast method for approximating frontal area projection from an STL mesh for vehicle aero/CFD calculations.

  Language:Python5

• nathanrooy/p-area

  The easiest way to compute the frontal/projected area of an STL geometry file. Useful for aerodynamic/CFD applications.

  Language:Python14

• wiewel/LatentSpacePhysics

  Towards Learning the Temporal Evolution of Fluid Flow

  Language:C++6419

• harsha070/Reconstruction-of-Flows

  Super resolution reconstruction in computational fluid dynamics. Proposed a novel autoencoder architecture to improve over existing results.

  Language:Jupyter Notebook74

• thunil/Deep-Flow-Prediction

  A framework for fluid flow (Reynolds-averaged Navier Stokes) predictions with deep learning

  Language:C++281117

• mdribeiro/DeepCFD

  DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks

  Language:Python18862

• LionelAgo/Vortex_AE

  Exploit Auto-encoder for exploring and predict flow dynamic

  Language:Jupyter Notebook102

• kfukami/CNN-SINDy-MLROM

  Sample codes of CNN-SINDy based reduced-order modeling for fluid flows by Fukami et al., JFM 2021.

  Language:Jupyter Notebook227

• opaliss/dmd_autoencoder

  Enhancing Dynamic Mode Decomposition using Autoencoder Networks.

  Language:Jupyter Notebook277

• JRice15/physics-informed-autoencoders

  Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics

  Language:Python315

• cfl-minds/twin_autoencoder

  A twin auto-encoder structure for flow prediction with built-in outlier detection and uncertainty prediction

  Language:Python1

• salehisaeed/semiImplicitSlip

  Language:C++91