NN-OpInf: an operator inference approach using structure-preserving composable neural networks

arXiv:2603.08488 · cs.LG, math.DS · Submitted 2026-03-09 · Read on arXiv

cs.LG, math.DS

Submitted: 2026-03-09

Updated: 2026-10-26

Terminology

• Accuracy — 2× in this paper · explained in 1 episode
• Composition — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 6 episodes

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks