CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis

2026-03-23

AI Summary

This work proposes a novel surrogate model that integrates neuroscience-inspired mechanisms with Bayesian deep operator networks to address the high computational cost of time-varying reliability analysis for nonlinear dynamical systems under stochastic excitation. By incorporating a brain-inspired neuronal architecture, the method enhances inference efficiency, while conformal prediction with partitioning ensures theoretically guaranteed uncertainty calibration. Leveraging the operator learning paradigm further improves generalization capability for high-dimensional, time-varying inputs. Experimental results demonstrate that the proposed model achieves high predictive accuracy and reliable coverage of failure probabilities across multiple nonlinear systems, significantly outperforming existing surrogate approaches in terms of scalability and energy efficiency.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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Abstract

Time-dependent reliability analysis of nonlinear dynamical systems under stochastic excitations is a critical yet computationally demanding task. Conventional approaches, such as Monte Carlo simulation, necessitate repeated evaluations of computationally expensive numerical solvers, leading to significant computational bottlenecks. To address this challenge, we propose \textit{CoNBONet}, a neuroscience-inspired surrogate model that enables fast, energy-efficient, and uncertainty-aware reliability analysis, providing a scalable alternative to techniques such as Monte Carlo simulations. CoNBONet, short for \textbf{Co}nformalized \textbf{N}euroscience-inspired \textbf{B}ayesian \textbf{O}perator \textbf{Net}work, leverages the expressive power of deep operator networks while integrating neuroscience-inspired neuron models to achieve fast, low-power inference. Unlike traditional surrogates such as Gaussian processes, polynomial chaos expansions, or support vector regression, that may face scalability challenges for high-dimensional, time-dependent reliability problems, CoNBONet offers \textit{fast and energy-efficient inference} enabled by a neuroscience-inspired network architecture, \textit{calibrated uncertainty quantification with theoretical guarantees} via split conformal prediction, and \textit{strong generalization capability} through an operator-learning paradigm that maps input functions to system response trajectories. Validation of the proposed CoNBONet for various nonlinear dynamical systems demonstrates that CoNBONet preserves predictive fidelity, and achieves reliable coverage of failure probabilities, making it a powerful tool for robust and scalable reliability analysis in engineering design.

Problem

• reliability analysis
• nonlinear dynamical systems
• stochastic excitations
• time-dependent reliability
• computational bottleneck

Innovation

• Conformal Prediction
• Neuroscience-inspired Computing
• Bayesian Operator Network
• Surrogate Modeling
• Time-dependent Reliability