Lalaland333

Lalaland333's Stars

• weihuayi/fealpy

  Finite Element Analysis Library in Python

  Language:Python283123

• mashaan14/ElsevierLatexTemplate

  A LaTeX Template with double column for Elsevier journals

  Language:TeX31

• cdc08x/letter-2-reviewers-LaTeX-template

  A LaTeX template to write response letters for journal revisions

  Language:TeX6122

• YimianDai/iNSFC

  An awesome LaTeX template for NSFC proposal.

  Language:TeX392184

• BIT-thesis/LaTeX-template

  LaTeX template for BIT thesis

  Language:TeX36292

• mohuangrui/ucasthesis

  LaTeX Thesis Template for the University of Chinese Academy of Sciences

  Language:TeX3.5k931

• Ruzim/NSFC-application-template-latex

  国家自然科学基金申请书正文（面上项目）LaTeX 模板（非官方）

  Language:TeX842205

• cmichi/latex-template-collection

  Collection of different LaTeX/XeTeX templates (cv, invoices, timesheets, letters, etc.).

  Language:TeX1.2k260

• Wandmalfarbe/pandoc-latex-template

  A pandoc LaTeX template to convert markdown files to PDF or LaTeX.

  Language:TeX6.2k960

• iagoac/elsevier

  LaTeX template for Elsevier journals

  Language:TeX2211

• 314arhaam/heat-pinn

  A Physics-Informed Neural Network to solve 2D steady-state heat equations.

  Language:Jupyter Notebook10213

• XavierNie715/PINN_Heat_Transfer

  PINN for heat transfer problems

  Language:Python104

• weihuayi/femprogramming

  Language:Python91

• idies/pyJHTDB

  Python wrapper for the Johns Hopkins turbulence database library

  Language:C9947

• shamsbasir/investigating_mitigating_failure_modes_in_pinns

  This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Networks(PINNs)"

  Language:Jupyter Notebook175

• Shengfeng233/PINN-for-turbulence

  A pytorch implementation of several approaches using PINN to slove turbulent flow

  Language:Python547

• jitinnair1/hello-phasefield

  some exercises in phasefield modelling solved in MATLAB

  Language:MATLAB212

• mathLab/PINA

  Physics-Informed Neural networks for Advanced modeling

  Language:Python37665

• vmattey/bc-PINN

  A Backward Compatible -- Physics Informed Neural Network for Allen Cahn and Cahn Hilliard Equations

  Language:Python245

• BryanKinzer/zener-pinning-PRISMS-PF

  Phase Field Zener Pinning Solidification PRISMS-PF Model

  Language:C++72

• idaholab/moose

  Multiphysics Object Oriented Simulation Environment

  Language:C++1.7k1k

• FreeFem/FreeFem-doc

  FreeFEM user documentation

  Language:JavaScript13143

• FreeFem/FreeFem-sources

  FreeFEM source code

  Language:C++765189

• okada39/pinn_cavity

  Physics informed neural network (PINN) for cavity flow governed by Navier-Stokes equation.

  Language:Python11930

• Alexzihaohu/NSFnets

  PINN in solving Navier–Stokes equation

  Language:Python7425

• idrl-lab/PINNpapers

  Must-read Papers on Physics-Informed Neural Networks.

  Language:Python881155

• maziarraissi/PINNs

  Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

  Language:Python3.8k1.3k

• ehsankharazmi/hp-VPINNs

  hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations

  Language:Python7232

• Lalaland333/test_c

  1

• singhsidhukuldeep/2D-Navier-Stokes-Solver

  As the field of Computational Fluid Dynamics (CFD) progresses, the fluid flows are more and more analysed by using simulations with the help of high speed computers. In order to solve and analyse these fluid flows we require intensive simulation involving mathematical equations which governs the fluid flow, these are Navier Stokes (NS) equation. Solving these equations has become a necessity as almost every problem which is related to fluid flow analysis call for solving of Navier Stokes equation. These NS equations are partial differential equations so different numerical methods are used to solve these equations. Solving these partial differential equations so different numerical methods requires large amount of computing power and huge amount of memory is in play. Only practical feasible way to solve these equation is write a parallel program to solve them, which can then be run on powerful hardware capable of parallel processing to get the desired results High speed supercomputer will provide us very good performance in terms of reduction in execution time. In paper focus will be on finite volume as a numerical method. We will also see what GPGPU (General-Purpose computing on Graphics Processing Units) is and how we are taking its advantages to solve CFD problems.

  Language:C2612