Continually learning neural-operator surrogate for three-dimensional airborne electromagnetic Bayesian inversion

arXiv:2608.25932 · physics.geo-ph, cs.LG · Submitted 2026-08-26 · Read on arXiv

physics.geo-ph, cs.LG

Submitted: 2026-08-26

Updated: 2026-08-26

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

Terminology

• Continual Learning — 12× in this paper · explained in 2 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Inverse Problem — 6× in this paper · explained in 2 episodes
• Distillation — 5× in this paper · explained in 2 episodes
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Physical Constraints — 2× in this paper · explained in 7 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptive Sampling — 1× in this paper · explained in 1 episode
• Anisotropy — 1× in this paper · explained in 3 episodes
• Bayesian Framework — 1× in this paper · explained in 3 episodes
• Bayesian Inference — 1× in this paper · explained in 5 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 5 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Covariate Shift — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Distilling the Knowledge in a Neural Network
• Fourier Neural Operator for Parametric Partial Differential Equations

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