Data-assisted reduced-order modeling of extreme events in complex dynamical systems

Machine learning of linear differential equations using Gaussian processes

(2017) Maziar Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Physics-informed machine learning approach for reconstructing Reynolds stress modeling discrepancies based on DNS data

(2017) Jian-Xun Wang et al.   Physical Review Fluids

A variational approach to probing extreme events in turbulent dynamical systems

(2017) Mohammad Farazmand et al.   Science Advances

Machine learning strategies for systems with invariance properties

(2016) Julia Ling et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Forecasting turbulent modes with nonparametric diffusion models: Learning from noisy data

(2016) Tyrus Berry et al.   PHYSICA D-NONLINEAR PHENOMENA

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Dynamic Mode Decomposition with Control

(2016) Joshua L. Proctor et al.   SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS

A statistical learning strategy for closed-loop control of fluid flows

(2016) Florimond Guéniat et al.   THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS

Nonparametric forecasting of low-dimensional dynamical systems

(2015) Tyrus Berry et al.   PHYSICAL REVIEW E

Highly Nonlinear Wave Propagation in Elastic Woodpile Periodic Structures

(2015) E. Kim et al.   PHYSICAL REVIEW LETTERS

Granular acoustic switches and logic elements

(2014) Feng Li et al.   Nature Communications

Invariant recurrent solutions embedded in a turbulent two-dimensional Kolmogorov flow

(2013) Gary J. Chandler et al.   JOURNAL OF FLUID MECHANICS

Physics-Based Learning Models for Ship Hydrodynamics

(2013) Gabriel D. Weymouth et al.   JOURNAL OF SHIP RESEARCH

Blended reduced subspace algorithms for uncertainty quantification of quadratic systems with a stable mean state

(2013) Themistoklis P. Sapsis et al.   PHYSICA D-NONLINEAR PHENOMENA

A statistically accurate modified quasilinear Gaussian closure for uncertainty quantification in turbulent dynamical systems

(2013) Themistoklis P. Sapsis et al.   PHYSICA D-NONLINEAR PHENOMENA

Compressive sensing based machine learning strategy for characterizing the flow around a cylinder with limited pressure measurements

(2013) Ido Bright et al.   PHYSICS OF FLUIDS

Rogue waves and their generating mechanisms in different physical contexts

(2013) M. Onorato et al.   PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS

Statistically accurate low-order models for uncertainty quantification in turbulent dynamical systems

(2013) Themistoklis P. Sapsis et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Recent progress in investigating optical rogue waves

(2013) N Akhmediev et al.   Journal of Optics

Challenges in Climate Science and Contemporary Applied Mathematics

(2012) Andrew J. Majda   COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS

Granularity and Inhomogeneity Are the Joint Generators of Optical Rogue Waves

(2011) F. T. Arecchi et al.   PHYSICAL REVIEW LETTERS

Distilling Free-Form Natural Laws from Experimental Data

(2009) Michael Schmidt et al.   SCIENCE

Rogue waves in oceanic turbulence

(2008) Francesco Fedele   PHYSICA D-NONLINEAR PHENOMENA