Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs

arXiv:2502.00550 · cs.LG, cs.NA, math.NA, physics.comp-ph · Submitted 2025-02-01 · Read on arXiv

cs.LG, cs.NA, math.NA, physics.comp-ph

Submitted: 2025-02-01

Updated: 2026-09-14

Comments: 33 pages, 17 figures

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

Terminology

• Fidelity — 37× in this paper · explained in 2 episodes
• Activation Function — 11× in this paper · explained in 1 episode
• Langevin Dynamics — 11× in this paper · explained in 2 episodes
• Accuracy — 9× in this paper · explained in 1 episode
• Stochastic Gradient Langevin Dynamics — 9× in this paper
• Ablation — 8× in this paper · explained in 1 episode
• Generalization — 8× in this paper · explained in 13 episodes
• Ablation Studies — 7× in this paper · explained in 1 episode
• Loss Function — 6× in this paper · explained in 2 episodes
• Active Learning — 5× in this paper · explained in 6 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Uncertainty Quantification — 4× in this paper · explained in 8 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Hamiltonian Monte Carlo — 3× in this paper
• Abundance — 2× in this paper · explained in 1 episode
• DeepONet — 2× in this paper · explained in 2 episodes
• Digital Twin — 2× in this paper · explained in 4 episodes
• Frequency-Domain Analysis — 2× in this paper · explained in 2 episodes
• Meta-learning — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptability — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Hysteresis — 1× in this paper · explained in 3 episodes
• Inverse Design — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Non-asymptotic Analysis — 1× in this paper · explained in 2 episodes
• Ordinary Differential Equations (ODEs) — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes

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