George Haller
@GeorgeHallerETH
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Professor of Nonlinear Dynamics at @ETH_en. Former faculty at @BrownUniversity, @MIT and @mcgillu Group page: https://t.co/yzv5n3vvRz
ETH Zürich, Switzerland
Joined January 2022
A 2D SSM-reduced model accurately predicts the motion of the full, nonlinear, finite-element model of a plate in a random pressure field. General theory of random SSMs (random spectral submanifolds) with examples here:
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Data-Driven Model Reduction for Dynamical Systems Montestigliano Spring School for Graduate Students 13th - 19th April 2025, Montestigliano, Italy Register till February 3, 2025
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We can now extract simple and predictive reduced-order models for nonlinear dynamical systems directly from videos!
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Puzzling period-3 response in forced fluid sloshing turns out to arise from a nonlinear resonance. Predicted by a data-driven, 4D SSM-reduced model, extracted directly from a video of the experiment.
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A Ph.D. position is available at ETH Zurich in Data-Driven Nonlinear Model Reduction (a.k.a. Dynamics-Based Machine Learning) and its applications to large-scale physical problems. Interested candidates may apply here:
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Data-driven linearization (DDL) explains dynamic mode decomposition (DMD) as a first-order, approximate linearization of the dynamics on a dominant spectral submanifold. DDL then refines this linearization to higher orders with increased accuracy.
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Model Reduction and Machine Learning for Solids, Fluids and Controls (Udine, Italy, Sept. 9-13). Summer school by Bala Balachandran, Charbel Farhat, Michael Graham, George Haller, Shobhit Jain and Gianluigi Rozza. Register here:
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RT @ArratiaPaulo: Bacterial Barriers (Part 1): New manuscript with @rrjs29 in @JFluidMech shows how interactions between swimming bacteria…
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Non-smooth dynamical systems, such as an oscillating beam rubbing against a wall, also have low-dimensional attracting spectral submanifolds (SSMs). But those SSMs, and the reduced models they carry, are only piecewise smooth. Movie: @leonardo_bttn Paper:
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RT @GeorgeHallerETH: We have just added a poster session to this CISM-EUROMECH advanced course: "Model reduction and machine learning for s…
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The power of data-driven spectral submanifold (SSM) reduction: Nonlinear oscillations of an elastic flag in counter flow, together with its reconstructed chaotic attractor within a 4D SSM. Obtained directly from the videos of the experiment.
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Thanks to Nonlinear Dynamics for selecting our new article as Editor's Choice!
NODY Editor's Choice. May 2024's 2nd issue Vol. 112(10) "Fast computation and characterization of forced response surfaces via spectral submanifolds and parameter continuation" 🚀 Exciting development in dynamic systems analysis! 💡 Discover how the Spectral SubManifolds method (SSM) computes Forced Response Surface (FRS) of high-dimensional systems, also with internal resonances. 🔄 Various examples showcase computational speed-up gains! 💻 #MechanicalSystems #SSM #Reducedordermodeling #ROM #SpringerNature #Nonlineardynamics
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@premashis_kumar Thanks. The methodology we have for chaos prediction generally works if chaos is created by known additive forcing to the system, as in the periodically forced finite-element beam example in our paper.
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We have just added a poster session to this CISM-EUROMECH advanced course: "Model reduction and machine learning for solids, fluids and controls" , Udine, Italy, September 9-13, 2024. Registered participants will have a chance to present their own work on model reduction.
CISM-EUROMECH advanced course: "Model reduction and machine learning for solids, fluids and controls" , Udine, Italy, September 9-13, 2024: Lecturers: Profs. Bala Balachandran, Charbel Farhat, Michael Graham, Shobhit Jain, Gianluigi Rozza and yours truly.
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