Integrating machine learning and process-based models for Earth and Environmental Sciences
- Start: Jun 4, 2024
- End: Jun 6, 2024
- Location: Max Planck Institute for Biogeochemistry
- Host: Martin Jung
June 4, Tuesday | June 5, Wednesday | June 6, Thursday | |
---|---|---|---|
09:30 - 11:00 |
1) Martin Jung: Welcome and Introduction
(10’)
|
Talks & discussion
|
Talks & discussion
1) Basil Kraft:
“Hybrid (?) data
-
driven river routing” (10’)
|
11:00 -11:30 | Coffee | Coffee | Coffee |
11:30 - 12:30 |
Talks & discussion
|
Talks & discussion
|
Joint recap & synthesis of break out discussion |
12:30 - 13:30 | Lunch | Lunch | Lunch |
13.30 - 15:00 |
Posters
|
Talks & discussion
1) Christian Reimers:
“Integrating prior knowledge into Stable Diffusion For Time Series Prediction” (20’)
|
Planning out specific activities and collaborations |
15:00 - 16:00 |
Talks & discussion
1) Zavud Baghirov:
“Hybrid Global Modeling of Water
-
Carbon cycles” (20’) |
Coffee Break & break out group discussion | Concluding remarks (until 15:30) |
16:00 -.17:00 | Joint discussion with coffe and snacks | break out group discussion | |
17:00 - 18:00 | Hike to Vollradisroda | ||
18:30 - 22:00 | Dinner & synthesis |
Relevant literature shared by participants
Yin et al. (2022): Augmenting Physical Models with Deep Networks for Complex Dynamics Forecasting
Serrano et al. (2023): Operator Learning with Neural Fields: Tackling PDEs on General Geometries
Doumèche et al. (2024): Physics-informed machine learning as a kernel method
Doumèche et al. (2023): Convergence and error analysis of PINNs
Doumèche et al. (2023): Human spatial dynamics for electricity demand forecasting: the case of France during the 2022 energy crisis
Shen et al. (2023): Differentiable modelling to unify machine learning and physical models for geosciences
Feng et al. (2022): Differentiable, Learnable, Regionalized Process-Based Models With Multiphysical Outputs can Approach State-Of-The-Art Hydrologic Prediction Accuracy
Tsai et al. (2021): From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
Bindas et al. (2024): Improving River Routing Using a Differentiable Muskingum-Cunge Model and Physics-Informed Machine Learning
Feng et al. (2023): Deep Dive into Global Hydrologic Simulations: Harnessing the Power of Deep Learning and Physics-informed Differentiable Models
Son et al. (2024): Integration of a Deep-Learning-Based Fire Model Into a Global Land Surface Model Bao et al. (2023): Toward Robust Parameterizations in Ecosystem-Level Photosynthesis Models
Yang et al. (2023): A flexible and efficient knowledge-guided machine learning data assimilation (KGML-DA) framework for agroecosystem prediction in the US Midwest
Liu et al. (2024): Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems
Kraft et al. (2022): Towards hybrid modelling of the global hydrological cycle
Reichstein et al. (2019): Deep learning and process understanding for data-driven Earth system
science
ElGhawi et al. (2023): Hybrid modelling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning