Uncertainty-aware neural emulation reveals climate-resilient maize traits at scale

arXiv:2605.22848 · cs.CE, cs.LG, q-bio.OT · Submitted 2026-05-15 · Read on arXiv

cs.CE, cs.LG, q-bio.OT

Submitted: 2026-05-15

Updated: 2026-09-26

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 9× in this paper · explained in 1 episode
• Uncertainty Quantification — 7× in this paper · explained in 8 episodes
• Fidelity — 6× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Bayesian Inference — 3× in this paper · explained in 5 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Core — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Latent Representation — 2× in this paper · explained in 2 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Robustness — 1× in this paper · explained in 5 episodes

Sources

• Surrogate impact modelling for crop yield assessment
• Deep Learning Meets Process-Based Models: A Hybrid Approach to Agricultural Challenges

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