Safe-by-Design Learning via Energy-based Neural Networks

arXiv:2609.36942 · cs.LG, cs.AI, cs.SY, eess.SY · Submitted 2026-09-29 · Read on arXiv

cs.LG, cs.AI, cs.SY, eess.SY

Submitted: 2026-09-29

Updated: 2026-09-30

Code: https://github.com/sim1bet/energy-safe-dynamics

Terminology

• Benchmark — 34× in this paper · explained in 2 episodes
• Robustness — 33× in this paper · explained in 5 episodes
• Accuracy — 14× in this paper · explained in 1 episode
• System Identification — 6× in this paper · explained in 3 episodes
• Verification — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Activation Function — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Coupled Electric Drive — 2× in this paper
• European Control Conference — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Lyapunov function — 2× in this paper · explained in 2 episodes
• Abstraction — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Analytic Activation Functions — 1× in this paper · explained in 1 episode
• Associative Memory — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Hybrid System — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Provenance — 1× in this paper · explained in 3 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes

Sources

• Stable Port-Hamiltonian Neural Networks
• Large Associative Memory Problem in Neurobiology and Machine Learning

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: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks