Understanding climate phenomena with data-driven models-Reference-Cited by-同舟云学术

Understanding climate phenomena with data-driven models

Published:2020-12 Issue: Volume:84 Page:46-56
ISSN:0039-3681
Container-title:Studies in History and Philosophy of Science Part A
language:en
Short-container-title:Studies in History and Philosophy of Science Part A

Author:

Knüsel Benedikt,Baumberger Christoph

Funder

Swiss National Science Foundation National Research Programme Big Data

Publisher

Elsevier BV

Subject

History and Philosophy of Science,History

Reference52 articles.

1. Understanding the drivers of marine liquid-water cloud occurrence and properties with global observations using neural networks;Andersen;Atmospheric Chemistry and Physics,2017

2. Explicating objectual understanding taking degrees seriously;Baumberger;Journal for General Philosophy of Science,2019

3. What is understanding? An overview of recent debates in epistemology and philosophy of science;Baumberger,2017

4. Building confidence in climate model projections: An analysis of inferences from fit;Baumberger;Wiley Interdisciplinary Reviews: Climate Change,2017

5. Random forests;Breiman;Machine Learning,2001

Cited by 19 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. For a Pluralism of Climate Modeling Strategies;Bulletin of the American Meteorological Society;2024-07

2. SIDEs: Separating Idealization from Deceptive 'Explanations' in xAI;The 2024 ACM Conference on Fairness, Accountability, and Transparency;2024-06-03

3. Conceptualizing understanding in explainable artificial intelligence (XAI): an abilities-based approach;Ethics and Information Technology;2024-06

4. Does the no miracles argument apply to AI?;Synthese;2024-05-13

5. The Explanatory Role of Machine Learning in Molecular Biology;Erkenntnis;2024-01-17