From 5970acdef176f942860304413c7b87753657e873 Mon Sep 17 00:00:00 2001
From: Noam Zeilberger <noam.zeilberger@gmail.com>
Date: Fri, 21 Mar 2025 16:42:32 +0100
Subject: [PATCH] typo
---
_data/seminar.yml | 2 +-
1 file changed, 1 insertion(+), 1 deletion(-)
diff --git a/_data/seminar.yml b/_data/seminar.yml
index f0e39c3..d92b40b 100644
--- a/_data/seminar.yml
+++ b/_data/seminar.yml
@@ -19,7 +19,7 @@
abstract: |
I present a method for computing statistically guaranteed confidence bands for functional surrogate modes: surrogate models which map between function spaces, motivated by the need build reliable physics emulators. The method constructs nested confidence sets on a low-dimensional representation (an SVD) of the surrogate model’s prediction error, and then maps these sets to the prediction space using set-propagation techniques. The result is conformal-like coverage guaranteed prediction sets for functional surrogate models. We use zonotopes as basis of the set construction, due to their well-studied set-propagation and verification properties. The method is model agnostic and can thus be applied to complex Sci-ML models, including Neural Operators, but also in simpler settings. An important step is a technique to capture the truncation error of the SVD, ensuring the guarantees of the method.
- A preprint is available here: [arXiv:arXiv.2501.18426](https://doi.org/10.48550/arXiv.2501.18426)
+ A preprint is available here: [arXiv.2501.18426](https://doi.org/10.48550/arXiv.2501.18426)
- date: 2025-03-24 14:00
team: PARTOUT
room: Marcel-Paul Schützenberger
--
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