From 9881e9b1257d017166da966fa219bf2715eeeb41 Mon Sep 17 00:00:00 2001
From: Sathvik Bhagavan <35105271+sathvikbhagavan@users.noreply.github.com>
Date: Sat, 29 Jun 2024 09:32:17 +0530
Subject: [PATCH] docs: add Zenodo DOI

---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 6e065a6d..0d76ff12 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,8 @@
 [![ColPrac: Contributor's Guide on Collaborative Practices for Community Packages](https://img.shields.io/badge/ColPrac-Contributor%27s%20Guide-blueviolet)](https://github.com/SciML/ColPrac)
 [![SciML Code Style](https://img.shields.io/static/v1?label=code%20style&message=SciML&color=9558b2&labelColor=389826)](https://github.com/SciML/SciMLStyle)
 
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.12571718.svg)](https://doi.org/10.5281/zenodo.12571718)
+
 A surrogate model is an approximation method that mimics the behavior of a computationally
 expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function
 `f(p)`, but each calculation of `f` is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. The idea is then to develop a surrogate model `g` which approximates `f` by training on previous data collected from evaluations of `f`.