Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis

no code implementations • 20 Dec 2023 • Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann

We argue that interpretations of machine learning (ML) models or the model-building process can bee seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics.

Position

Differentiable modeling to unify machine learning and physical models and advance Geosciences

no code implementations • 10 Jan 2023 • Chaopeng Shen, Alison P. Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi, Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J. Harman, Martyn Clark, Matthew Farthing, Dapeng Feng, Praveen Kumar, Doaa Aboelyazeed, Farshid Rahmani, Hylke E. Beck, Tadd Bindas, Dipankar Dwivedi, Kuai Fang, Marvin Höge, Chris Rackauckas, Tirthankar Roy, Chonggang Xu, Binayak Mohanty, Kathryn Lawson

Here we present differentiable geoscientific modeling as a powerful pathway toward dissolving the perceived barrier between them and ushering in a paradigm shift.