7 papers · 1 filter
DySCo: Dynamically consistent data-driven downscaling of extremes in climate projections
S. Stamatelopoulos, M. Wang, I. Lopez-Gomez +5
Regional climate risk assessment is critical for applications such as infrastructure design, disaster forecasting, and insurance resource allocation. However, estimating regional (…
Dynamics-Informed Deep Learning for Predicting Extreme Events
Eirini Katsidoniotaki, Themistoklis P. Sapsis
Predicting extreme events in high-dimensional chaotic dynamical systems remains a fundamental challenge, as such events are rare, intermittent, and arise from transient dynamical m…
On Some Tunable Multi-fidelity Bayesian Optimization Frameworks
Arjun Manoj, Anastasia S. Georgiou, Dimitris G. Giovanis +2
Multi-fidelity optimization employs surrogate models that integrate information from varying levels of fidelity to guide efficient exploration of complex design spaces while minimi…
A probabilistic framework for learning non-intrusive corrections to long-time climate simulations from short-time training data
Benedikt Barthel Sorensen, Leonardo Zepeda-Núñez, Ignacio Lopez-Gomez +4
Chaotic systems, such as turbulent flows, are ubiquitous in science and engineering. However, their study remains a challenge due to the large range scales, and the strong interact…
Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models
Ethan Pickering, Themistoklis P. Sapsis
Misleading or unnecessary data can have out-sized impacts on the health or accuracy of Machine Learning (ML) models. We present a Bayesian sequential selection method, akin to Baye…
Active search for Bifurcations
Yorgos M. Psarellis, Themistoklis P. Sapsis, Ioannis G. Kevrekidis
Bifurcations mark qualitative changes of long-term behavior in dynamical systems and can often signal sudden ("hard") transitions or catastrophic events (divergences). Accurately l…