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Found 8 papers, 6 papers with code
A non-intrusive machine learning framework for debiasing long-time coarse resolution climate simulations and quantifying rare events statistics
To overcome this challenge, we introduce a dynamical systems approach where the correction operator is trained using reference data and a coarse model simulation nudged towards that reference.
A Multi-Scale Deep Learning Framework for Projecting Weather Extremes
Weather extremes are a major societal and economic hazard, claiming thousands of lives and causing billions of dollars in damage every year.
Output-weighted and relative entropy loss functions for deep learning precursors of extreme events
One cause for this difficulty is that systems with extreme events, by definition, yield imbalanced datasets and that standard loss functions easily ignore rare events.
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
We present a new type of acquisition functions for online decision making in multi-armed and contextual bandit problems with extreme payoffs.
Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification
We introduce a class of acquisition functions for sample selection that leads to faster convergence in applications related to Bayesian experimental design and uncertainty quantification.
Informative Path Planning for Extreme Anomaly Detection in Environment Exploration and Monitoring
An unmanned autonomous vehicle (UAV) is sent on a mission to explore and reconstruct an unknown environment from a series of measurements collected by Bayesian optimization.
Bayesian Optimization with Output-Weighted Optimal Sampling
In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy estimations.
Generative stochastic modeling of strongly nonlinear flows with non-Gaussian statistics
As such, this framework represents the chaotic time series as the evolution of a stochastic system observed through the lens of a nonlinear map.
Dynamical Systems Chaotic Dynamics 62G32, 76F20, 49Q22, 60G10
