software

Introduction

I recently posted what was obtained by PINNs for steady-state laminar flow. This time, I applied the PINNs method to unsteady laminar flows. The results of this time are not correct and different from the results of the referenced paper. I am investigating the cause, but I do not know.

Introduction

I have been trying to simulate steady state laminar flows with PINNs since the end of last year, but I was stumped by the derivation of the Navier Stokes (NS) equations of Cauchy stress tensor type. I coded PINNs with the governing equations based on the Velocity-pressure type NS equations. I got a result that looks like it, and I will summarize it in this post.

Introduction

Continuing from yesterday, I tried the book example (Chapter 4 of the book). It was quite interesting.

Introduction

Yesterday, I installed OpenFOAM and ParaView and tried to simulate and visualize a Poiseuille flow (Hagen-poiseuille flow). This time, I followed the book and ran “cavity” from the OpenFOAM built-in tutorial.

Motivation

I have been working on PINNs since the end of last year and have attempted two problems. I am planning to select and work on problems in the field of fluid dynamics. In fact, I am working on incompressible flow around a cylinder, but I have been stuck for a week because I can’t figure out how to relate the output of the model to the differential equations (the key part of PINNs).

I decided to learn a bit of OpenFOAM if I am going to work on the fluid dynamics area, so I installed OpenFOAM, ParaView, and tried the examples.

Introduction

Recently, I summarized the mass, spring, and damper problem with PINNs. This time, I’ve worked on the Burgers’ equation and summarized it.

Motivation

I tried PINNs (Physics-Informed Neural Networks) with NVIDIA Modulus over a year ago, After that, it was completely untouched. I have heard Riken/Matsuoka-san’s talk at a recent seminar (AI for Science; here and here), I decided to study it again and tried to implement PINNs using a physically understandable problem as an example.

Introduction

It has already been a week since I created the knowledge graph in my local environment and started building the GraphRAG environment. It is finally in a decent working condition. This post is a summary of GraphRAC using Ollama and neo4j that I built in my local environment.

Motivation

I was trying a graph RAG and got an error “TypeError: ‘NoneType’ object is not iterable”. I did a lot of research, but could not find the direct cause of the error and just could not get around it. The python version of the container I was using at the time was 3.10.12. The version of python in the article I was referring to was 3.11.0, so I decided to update the python version of the container to 3.11 as a trial.

This post summarizes the creation of the JupyterLab container with a python version of 3.11.

はじめに

In this post, I created a knowledge graph as a starting point for building a RAG system. After trying several different LLMs, only gpt-4o and gpt-4o-mini worked properly. In a recent this post, I summarized the installation and activation of ollama.

I created a knowledge graph using ollama this time, and I summarize its contents here. I hope this will be helpful, as I got into a bit of trouble in some areas.