SEAM: Global consistency beyond local accuracy in scientific machine learning

arXiv:2608.05702 · cs.LG, cs.CE · Submitted 2026-08-06 · Read on arXiv

Gnankan Landry Regis N'guessan, Bum Jun Kim

cs.LG, cs.CE

Submitted: 2026-08-06

Comments: 43 pages, 9 figures

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

Terminology

• Consistency — 72× in this paper · explained in 3 episodes
• Generator — 51× in this paper · explained in 2 episodes
• Accuracy — 50× in this paper · explained in 1 episode
• Admissibility — 50× in this paper · explained in 2 episodes
• Calibration — 9× in this paper · explained in 9 episodes
• Monotonicity — 7× in this paper · explained in 2 episodes
• Robustness — 7× in this paper · explained in 6 episodes
• Distribution Shift — 6× in this paper · explained in 12 episodes
• Inverse Problem — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Neural Operators — 5× in this paper · explained in 2 episodes
• Provenance — 5× in this paper · explained in 5 episodes
• Conservation Laws — 4× in this paper · explained in 3 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Data Provenance — 3× in this paper · explained in 3 episodes
• Scientific Explanation-Admissibility Machines — 3× in this paper
• DeepONet — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fourier Neural Operator (FNO) — 2× in this paper · explained in 3 episodes
• Localization — 2× in this paper · explained in 2 episodes
• Sparsity — 2× in this paper · explained in 5 episodes
• Temporal Consistency — 2× in this paper · explained in 3 episodes
• XGBoost — 2× in this paper · explained in 3 episodes
• Absorb — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Compositionality — 1× in this paper · explained in 2 episodes
• Concept Drift — 1× in this paper · explained in 6 episodes
• Covariate Shift — 1× in this paper · explained in 3 episodes
• District of Columbia — 1× in this paper
• Feature Engineering — 1× in this paper · explained in 3 episodes
• GNNs — 1× in this paper · explained in 2 episodes
• Gauge Invariance — 1× in this paper · explained in 2 episodes
• Non-stationarity — 1× in this paper · explained in 2 episodes
• Orthogonal Projection — 1× in this paper · explained in 2 episodes
• PDE residual — 1× in this paper · explained in 2 episodes
• Predictive Modeling — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Spectral Gap — 1× in this paper · explained in 6 episodes
• Training Objective — 1× in this paper · explained in 3 episodes
• Training Objectives — 1× in this paper · explained in 2 episodes
• United States Geological Survey — 1× in this paper
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Sheaf Neural Networks
• Universal Differential Equations for Scientific Machine Learning

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