[2603.21678] CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis

Statistics > Machine Learning arXiv:2603.21678 (stat) [Submitted on 23 Mar 2026] Title:CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis Authors:Shailesh Garg, Souvik Chakraborty View a PDF of the paper titled CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis, by Shailesh Garg and Souvik Chakraborty View PDF HTML (experimental) 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 reli...