From 8961be43b4b506eb837b7009b0e7b08f27ee1754 Mon Sep 17 00:00:00 2001 From: "qiangliu.7@outlook.com" Date: Tue, 13 Aug 2024 09:49:44 +0200 Subject: [PATCH] fix README --- README.md | 10 +++++----- mkdocs.yml | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 62cdac8..5b8fafb 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@
Towards Conflict-free Training for everything!

- [Arxiv]•[📖 Documentation & Examples] + [Arxiv]•[📖 Documentation & Examples]

## About @@ -22,7 +22,7 @@ ​ The ConFIG method obtains the conflict-free direction by calculating the inverse of the loss-specific gradients matrix: $$ -\boldsymbol{g}_{ConFIG}=\left(\sum_{i=1}^m \boldsymbol{g}_i^\top\boldsymbol{g}_u\right)\boldsymbol{g}_u, +\boldsymbol{g}_{ConFIG}=\left(\sum_{i=1}^{m} \boldsymbol{g}_{i}^\top\boldsymbol{g}_{u}\right)\boldsymbol{g}_u, $$ $$ @@ -30,7 +30,7 @@ $$ [\mathcal{U}(\boldsymbol{g}_1),\mathcal{U}(\boldsymbol{g}_2),\cdots, \mathcal{U}(\boldsymbol{g}_m)]^{-\top} \mathbf{1}_m\right]. $$ -Then the dot product between $\boldsymbol{g}_{ConFIG}$ and each loss-specific gradient is always positive and equal, i.e., $\boldsymbol{g}_i^\top\boldsymbol{g}_{ConFIG}=\boldsymbol{g}_i^\top\boldsymbol{g}_{ConFIG} \quad \forall i,j \in [1,m]$​. +Then the dot product between $\boldsymbol{g}_{ConFIG}$ and each loss-specific gradient is always positive and equal, i.e., $\boldsymbol{g}_{i}^{\top}\boldsymbol{g}_{ConFIG}=\boldsymbol{g}_{i}^{\top}\boldsymbol{g}_{ConFIG} \quad \forall i,j \in [1,m]$​. * **Is the ConFIG Computationally expensive?** @@ -39,7 +39,7 @@ Then the dot product between $\boldsymbol{g}_{ConFIG}$ and each loss-specific gr ## Paper Info

ConFIG: Towards Conflict-free Training of Physics Informed Neural Networks

-
Qiang Liu, Mengyu Chu and Nils Thuerey
+
Qiang Liu, Mengyu Chu and Nils Thuerey
Technical University of Munich @@ -101,7 +101,7 @@ for input_i in dataset: optimizer.step() ``` -More details and examples can be found in our [doc page](https://qiauil.github.io/ConFIG/). +More details and examples can be found in our [doc page](https://tum-pbs.github.io/ConFIG/). To reproduce the result in our paper, please check the [experiments](https://github.com/tum-pbs/ConFIG/tree/main/experiments) folder. diff --git a/mkdocs.yml b/mkdocs.yml index 089d5a3..9c298d9 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,7 +1,7 @@ site_name: ConFIG site_description: Official Implementation for Conflict-Free Inverse Gradients Method. site_author: Qiang Liu -site_url: https://qiauil.github.io/ConFIG +site_url: https://tum-pbs.github.io/ConFIG repo_url: https://github.com/tum-pbs/ConFIG repo_name: tum-pbs/ConFIG