Composite Bayesian Optimization in function spaces using NEON—Neural Epistemic Operator Networks
Abstract Operator learning is a rising field of scientific computing where inputs or outputs of a machine learning model are functions defined in infinite-dimensional spaces. In this paper, we introduce Neon (Neural Epistemic Operator Networks), an architecture for generating predictions with uncert...
Saved in:
Main Authors: | Leonardo Ferreira Guilhoto, Paris Perdikaris |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Portfolio
2024-11-01
|
Series: | Scientific Reports |
Subjects: | |
Online Access: | https://doi.org/10.1038/s41598-024-79621-7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification
by: Masoud Muhammed Hassan, et al.
Published: (2025-01-01) -
Uncertainty quantification in multi-parametric MRI-based meningioma radiotherapy target segmentation
by: Lana Wang, et al.
Published: (2025-01-01) -
L’économie standard est-elle soluble dans le dialogue interdisciplinaire ?
by: Sylvain Maechler
Published: (2021-05-01) -
Bayesian calibration and uncertainty quantification for a large nutrient load impact modelZenodoZenodoZenodo
by: Karel Kaurila, et al.
Published: (2025-03-01) -
Decent Estimate of CME Arrival Time From a Data‐Assimilated Ensemble in the Alfvén Wave Solar Atmosphere Model (DECADE‐AWSoM)
by: Hongfan Chen, et al.
Published: (2025-01-01)