Data-driven prediction in dynamical systems: recent developments
A Ghadami, BI Epureanu - Philosophical Transactions of …, 2022 - royalsocietypublishing.org
In recent years, we have witnessed a significant shift toward ever-more complex and ever-
larger-scale systems in the majority of the grand societal challenges tackled in applied …
larger-scale systems in the majority of the grand societal challenges tackled in applied …
Extreme events in dynamical systems and random walkers: A review
Extreme events gain the attention of researchers due to their utmost importance in various
contexts ranging from climate to brain. An observable that deviates significantly from its long …
contexts ranging from climate to brain. An observable that deviates significantly from its long …
Statistics of extreme events in fluid flows and waves
TP Sapsis - Annual Review of Fluid Mechanics, 2021 - annualreviews.org
Extreme events in fluid flows, waves, or structures interacting with them are critical for a wide
range of areas, including reliability and design in engineering, as well as modeling risk of …
range of areas, including reliability and design in engineering, as well as modeling risk of …
Using machine learning to predict extreme events in complex systems
D Qi, AJ Majda - Proceedings of the National Academy of …, 2020 - National Acad Sciences
Extreme events and the related anomalous statistics are ubiquitously observed in many
natural systems, and the development of efficient methods to understand and accurately …
natural systems, and the development of efficient methods to understand and accurately …
User-defined event sampling and uncertainty quantification in diffusion models for physical dynamical systems
Diffusion models are a class of probabilistic generative models that have been widely used
as a prior for image processing tasks like text conditional generation and inpainting. We …
as a prior for image processing tasks like text conditional generation and inpainting. We …
Extreme events in globally coupled chaotic maps
Understanding and predicting uncertain things are the central themes of scientific evolution.
Human beings revolve around these fears of uncertainties concerning various aspects like a …
Human beings revolve around these fears of uncertainties concerning various aspects like a …
[HTML][HTML] Global, high-resolution mapping of tropospheric ozone–explainable machine learning and impact of uncertainties
C Betancourt, TT Stomberg, AK Edrich… - Geoscientific Model …, 2022 - gmd.copernicus.org
Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution
which is challenging to map on a global scale. Here, we present a data-driven ozone …
which is challenging to map on a global scale. Here, we present a data-driven ozone …
Extreme events prediction from nonlocal partial information in a spatiotemporally chaotic microcavity laser
The forecasting of high-dimensional, spatiotemporal nonlinear systems has made
tremendous progress with the advent of model-free machine learning techniques. However …
tremendous progress with the advent of model-free machine learning techniques. However …
Optimized ensemble deep learning framework for scalable forecasting of dynamics containing extreme events
The remarkable flexibility and adaptability of both deep learning models and ensemble
methods have led to the proliferation for their application in understanding many physical …
methods have led to the proliferation for their application in understanding many physical …
Distance dependent competitive interactions in a frustrated network of mobile agents
SN Chowdhury, S Majhi… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Diverse collective dynamics emerge in dynamical systems interacting on top of complex
network architectures. Along this line of research, temporal network has come out to be one …
network architectures. Along this line of research, temporal network has come out to be one …