surrogates

NVExplain introduces a model-agnostic framework for explaining time series forecasting by analyzing latent trajectories and semantic flow, using structure-preserving surrogates to generate human-readable explanations with competitive faithfulness and stability.

This paper presents a comparative study of Monte Carlo Dropout and Deep Ensemble methods for uncertainty quantification in AI-driven crash simulation surrogates, using an open-source bumper beam benchmark.

This paper proposes using language models as selective surrogates to optimize GPU kernel runtime, demonstrating a novel approach to performance forecasting.