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Ilija Trajković

Collaborateur·rice de recherche - Karlsruhe Institute of Technology
Superviseur⋅e principal⋅e
David Rolnick
Sujets de recherche
Causalité
GitHub

Publications

Causal Climate Emulation with Bayesian Filtering
Sebastian H. M. Hickman
Ilija Trajković
Julia Kaltenborn
Francis Pelletier
Alex Archibald
Yaniv Gurwicz
Peer Nowack
David Rolnick
Julien Boussard
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These … (voir plus)simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physics-informed causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a physics-informed approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.
2025-06-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Causal Climate Emulation with Bayesian Filtering
Sebastian H. M. Hickman
Ilija Trajković
Julia Kaltenborn
Francis Pelletier
Alex Archibald
Yaniv Gurwicz
Peer Nowack
David Rolnick
Julien Boussard
Traditional models of climate change use complex systems of coupled equations to simulate physical processes across the Earth system. These … (voir plus)simulations are highly computationally expensive, limiting our predictions of climate change and analyses of its causes and effects. Machine learning has the potential to quickly emulate data from climate models, but current approaches are not able to incorporate physics-informed causal relationships. Here, we develop an interpretable climate model emulator based on causal representation learning. We derive a physics-informed approach including a Bayesian filter for stable long-term autoregressive emulation. We demonstrate that our emulator learns accurate climate dynamics, and we show the importance of each one of its components on a realistic synthetic dataset and data from two widely deployed climate models.
2025-06-11
ArXiv (prépublication)
doi.org
arxiv.org