Machine Learning‐Based Parametrization of Gaseous Optical Properties and VANET Anomaly Detection for Accelerated Climate and Cyber‐Physical Simulations

This investigation delineates a dual‐domain computational paradigm integrating machine learning (ML)‐based parametrization of gaseous optical properties with ensemble‐driven anomaly detection in Vehicular Ad Hoc Networks (VANETs), aimed at accelerating predictive fidelity in climate‐cyber‐physical simulations. The study formulates a Random Forest (RF) surrogate model to approximate nonlinear radiative transfer coefficients and simultaneously classify spatiotemporally correlated anomalies induced by cyber‐physical perturbations within VANET topologies. A synthetically constructed dataset comprising 500 multi‐dimensional vehicular communication profiles encoding instantaneous velocity vectors, geospatial coordinates, signal‐to‐noise ratios, inter‐vehicular latency, and communication fidelity metrics was employed for rigorous model training and validation. The surrogate framework achieved a coefficient of determination () of 0.963, Mean Squared Error (MSE) of , and Mean Absolute Error (MAE) of 0.028, reflecting sub‐3% deviation from empirically derived ground truth in both anomaly classification and optical property approximation. Notably, latency analysis indicated a 35% reduction relative to conventional rule‐based intrusion detection paradigms, underscoring the potential of ensemble‐based surrogate learning for real‐time, computationally efficient detection of VANET anomalies under high‐dimensional, stochastic environments. Prospective enhancements include hybridization with temporal convolution networks and federated learning strategies to enable edge‐resident deployment while preserving high‐resolution optical and cyber‐physical predictive accuracy. This study substantively demonstrates the feasibility of co‐optimized surrogate modeling frameworks for synergistic acceleration of climate simulation fidelity and cyber‐physical system resilience.