A life-cycle optimization framework for deteriorating infrastructure under hazards is formulated as an MDP with a Kronecker-factored tensor method that reduces computational complexity from exponential to linear while preserving exact dynamic programming solutions.
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An adaptive digital twin uses online Bayesian updates on transition probabilities in dynamic Bayesian networks, combined with reinforcement learning on parametric MDPs, to enable personalized predictive decision-making for structural health monitoring.
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Probabilistic Hazard Analysis Framework with Stochastic Optimal Control for Deteriorating Civil Infrastructure Systems
A life-cycle optimization framework for deteriorating infrastructure under hazards is formulated as an MDP with a Kronecker-factored tensor method that reduces computational complexity from exponential to linear while preserving exact dynamic programming solutions.
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Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics
An adaptive digital twin uses online Bayesian updates on transition probabilities in dynamic Bayesian networks, combined with reinforcement learning on parametric MDPs, to enable personalized predictive decision-making for structural health monitoring.