Michael Graham

Steenbock Professor of Engineering

University of Wisconsin-Madison

United States

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Michael Graham is the Steenbock Professor of Engineering at the University of Wisconsin-Madison, USA. His research focuses on data-driven modeling in complex flows, particularly the application of manifold dynamics and the Koopman operator in predicting time-evolution. Recent articles highlight his work on elastoinertial turbulence, vascular dynamics in sickle cell disease, and low-dimensional modeling of dynamical systems.

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Collaborators (2)

Cristian Ricardo Constante Amores

University of Wisconsin-Madison

UNITED STATES

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Xiaopo Cheng

University of Wisconsin-Madison

UNITED STATES

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Michael Graham

Research Interests

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About

Articles (25)

On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions

Elastoinertial turbulence: data-driven reduced-order model based on manifold dynamics

Building symmetries into data-driven manifold dynamics models for complex flows

Red blood cell partitioning and segregation through vascular bifurcations in a model of sickle cell disease

Data-driven state-space and Koopman operator models of coherent state dynamics on invariant manifolds

Enhancing predictive capabilities in data-driven dynamical modeling with automatic differentiation: Koopman and neural ODE approaches

Data-driven low-dimensional model of a sedimenting flexible fiber

Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems

Free-space and near-wall dynamics of a flexible sheet sedimenting in Stokes flow

Flow instabilities in circular Couette flow of wormlike micelle solutions with a reentrant flow curve

Less-deformable erythrocyte subpopulations biomechanically induce endothelial inflammation in sickle cell disease

Nested travelling wave structures in elastoinertial turbulence

Predicting extreme events in a data-driven model of turbulent shear flow using an atlas of charts

Self-similar, spatially localized structures in turbulent pipe flow from a data-driven wavelet decomposition

Scattering-Informed Microstructure Prediction during Lagrangian Evolution (SIMPLE)—a data-driven framework for modeling complex fluids in flow

Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning

Data-driven low-dimensional dynamic model of Kolmogorov flow

Deep learning delay coordinate dynamics for chaotic attractors from partial observable data

Marginated aberrant red blood cells induce pathologic vascular stress fluctuations in a computational model of hematologic disorders

Effect of polymer additives on dynamics of water level in an open channel

Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow

Data-driven discovery of intrinsic dynamics

Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning

Data-driven reduced-order modeling of spatiotemporal chaos with neural ordinary differential equations

Wrinkling and multiplicity in the dynamics of deformable sheets in uniaxial extensional flow

Collaborators (2)

Cristian Ricardo Constante Amores

Xiaopo Cheng

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