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’)
2) Chaopeng Shen: “Differentiable modeling to improve global hydrologic modeling and water management” (30’)
3) Olivier Bonte: “Differentiable modelling for terrestrial evaporation: the GLEAM perspective” (10’)

Talks & discussion
1) Patrick Gallinari:
“Operator learning with neural fields : solving differential equations on general geometries” (30’)
2) Qi Yang: “Advancing flexibility and efficiency in ecosystem data assimilation via know ledge- guided machine learning” (10’)

Talks & discussion

1) Basil Kraft: “Hybrid (?) data - driven river routing” (10’)
2) Vitus Benson: “Hybrid Atmospheric Transport” (10’)
3) Maximilian Gelbrecht: “Towards stable hybrid atmospheric models: PseudoSpectralNet” (10’)

Joint recap & synthesis of break out discussion
11:00 -11:30 Coffee Coffee Coffee
11:30 - 12:30

Talks & discussion
1) Zhenong Jin:
„Knowledge -guided machine learning for the next generation of agroecosystem prediction” (30’)
2) Alexander Winkler: “Hybrid Modelling of Coupled Climate-Carbon-Cycle Interactions Using Neural ODEs” (10’)

Talks & discussion
1) Nathan Doumèche:
“Some statistical insights on Physics - Informed Machine Learning” (30’)
2) Lazaro Alonso: “Introduction to mixed -mode automatic differential in Julia” (10’)

Joint recap & synthesis of break out discussion
12:30 - 13:30 Lunch Lunch Lunch
13.30 - 15:00

1) Bernhard Ahrens:
„Parameter learning with a soil carbon model”
2) Shijie Jiang: „Hybrid and interpretable machine learning for understanding extreme hydroclimatic events”
3) Jiaxin Xie: „Can data streams from empirical estimation improve hybrid modeling?”
4) Maha Badri & Philipp Hess: “Towards a Hybrid Vegetation Model”
5) Nuno Carvalhais: “Integrating machine learning and mechanistic models for process abstraction and spatial parametrization towards an improved representation of carbon cycle dynamics across scales”
6) Laurent Bataille: tbd

Talks & discussion

1) Christian Reimers: “Integrating prior knowledge into Stable Diffusion For Time Series Prediction” (20’)
2) Markus Reichstein: “Some reflections on critical aspects of hybrid modelling” (20’)

Planning out specific activities and collaborations
15:00 - 16:00 Talks & discussion

1) Zavud Baghirov: “Hybrid Global Modeling of Water - Carbon cycles” (20’)
2) Manuel Alvarez Chaves: “E
valuating physics-based representations of hydrological systems through hybrid models and Information Theory” (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


ElGhawi et al. (2023): Hybrid modelling of evapotranspiration: inferring stomatal and aerodynamic resistances using combined physics-based and machine learning

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