基于能量神经网络的按设计安全学习
Safe-by-Design Learning via Energy-based Neural Networks
这篇论文介绍了一种新型神经网络架构，能在设计阶段就保证系统安全性，比现有方法更鲁棒。
研究人员提出了一种新型神经网络架构，基于现代Hopfield网络和port-Hamiltonian神经ODE，可在设计阶段就保证安全性。该框架在12维纳米无人机模型等多个基准测试中实现了最先进性能，并能产生比现有方法更鲁棒的认证不变集。新方法能够构建明确的允许输入集和定量鲁棒半径，无需昂贵的后验验证。
Safe-by-Design Learning via Energy-based Neural Networks
Learning neural-network models of dynamical systems with safety guarantees is a fundamental requirement for their deployment in safety-critical settings. Safety is commonly established by proving the invariance of a desired subset in state-space, ensuring that every trajectory initialized in this subset remains confined to it for all time under admissible inputs. Existing frameworks, however, either rely on computationally expensive post-hoc verification or employ safety-enforcing mechanisms without formal correctness guarantees. In this paper, we introduce a novel neural architecture grounded in energy-based modern Hopfield networks to guarantee safety-by-design while retaining sufficient expressiveness to model complex nonlinear dynamics. Specifically, we integrate modern Hopfield networks with a port-Hamiltonian neural ODE, enabling by design the construction of barrier functions yielding explicit admissible-input sets and quantitative robustness radii. Across several benchmarks, including an 12-dimensional nanodrone model, our framework achieves state-of-the-art performance while producing certified invariant sets that are more robust to external solicitations than comparable existing approaches.
