arXiv:2606.19302v1 Announce Type: cross Abstract: As deep learning for physical systems continues to grow in popularity, efforts to improve generalizability have primarily focused on designing architectures that embed physical constraints. However, for machine-learning surrogate climate models (emulators), we show that the low structural diversity in existing scenarios commonly used to generate training data places a ceiling on predictive skill. Here, we examine whether training datasets themselves can be optimized to improve generalization. We introduce a method to create datasets that produc
The increasing popularity of deep learning for physical systems, alongside efforts to improve generalizability, makes optimizing training data for climate models a natural next step.
Improving the accuracy and generalizability of AI-powered climate models has significant implications for climate prediction, policy, and research, moving beyond architectural constraints to data optimization.
The focus for improving AI climate emulators shifts from solely architectural design to include optimizing the training datasets themselves, potentially unlocking greater predictive skill.
- · Climate scientists
- · AI model developers
- · Environmental research institutions
- · Climate policy makers
- · Climate models with low structural diversity
- · Organizations relying on suboptimal climate projections
More accurate and reliable climate change predictions become possible due to optimized AI models.
Improved predictive capabilities allow for more effective climate mitigation strategies and resource allocation.
Greater confidence in climate models could accelerate investment in climate-resilient infrastructure and green technologies globally.
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Read at arXiv cs.LG