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Found 5 papers, 2 papers with code
Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization
Multi-agent simulation is commonly used across multiple disciplines, specifically in artificial intelligence in recent years, which creates an environment for downstream machine learning or reinforcement learning tasks.
Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems
Rare-event simulation techniques, such as importance sampling (IS), constitute powerful tools to speed up challenging estimation of rare catastrophic events.
Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set
Stochastic simulation aims to compute output performance for complex models that lack analytical tractability.
Rare-Event Simulation for Neural Network and Random Forest Predictors
We study rare-event simulation for a class of problems where the target hitting sets of interest are defined via modern machine learning tools such as neural networks and random forests.
Deep Probabilistic Accelerated Evaluation: A Robust Certifiable Rare-Event Simulation Methodology for Black-Box Safety-Critical Systems
Evaluating the reliability of intelligent physical systems against rare safety-critical events poses a huge testing burden for real-world applications.
