Elucidating the Conformal Structure of the Brinkman Penalisation Method for Geometry-Adapted, Structure-Preserving Operator Learning of Hamiltonian PDEs

arXiv:2609.29847 · math.NA, cs.LG, cs.NA · Submitted 2026-09-24 · Read on arXiv

math.NA, cs.LG, cs.NA

Submitted: 2026-09-24

Updated: 2026-09-24

Terminology

• Accuracy — 3× in this paper · explained in 1 episode
• Conservation Laws — 2× in this paper · explained in 3 episodes
• DeepONet — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Consistency — 1× in this paper · explained in 3 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Fidelity — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Neural Surrogates — 1× in this paper · explained in 2 episodes
• System Identification — 1× in this paper · explained in 3 episodes

Sources

• Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems

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