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VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification
Generative models are transforming science and engineering by enabling efficient synthetization and exploration of new scenarios for …
Paolo Conti, Jonas Kneifl, Andrea Manzoni, Attilio Frangi, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
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VENI, VINDy, VICI: A generative reduced-order modeling framework with uncertainty quantification
Data-driven identification of latent port-Hamiltonian systems
Conventional physics-based modeling techniques involve high effort, e.g., time and expert knowledge, while data-driven methods often …
Johannes Rettberg, Jonas Kneifl, Julius Herb, Patrick Buchfink, Jörg Fehr, Bernard Haasdonk
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Data-driven identification of latent port-Hamiltonian systems
Accelerated Non‐linear Stability Analysis Based on Predictions From Data‐Based Surrogate Models
Anika Strauß, Jonas Kneifl, Anton Tkachuk, Jörg Fehr, Manfred Bischoff
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Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks
Highly nonlinear dynamic finite element simulations using explicit time integration are particularly valuable tools for structural …
Jonas Kneifl, Jörg Fehr, Steven L. Brunton, J. Nathan Kutz
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Multi-hierarchical surrogate learning for explicit structural dynamical systems using graph convolutional neural networks
On using machine learning algorithms for motorcycle collision detection
Globally, motorcycles attract vast and varied users. However, since the rate of severe injury and fatality in motorcycle accidents far …
Philipp Rodegast, Steffen Maier, Jonas Kneifl, Jörg Fehr
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Low-dimensional data-based surrogate model of a continuum-mechanical musculoskeletal system based on non-intrusive model order reduction
Over the last decades, computer modeling has evolved from a supporting tool for engineering prototype design to an ubiquitous …
Jonas Kneifl, David Rosin, Okan Avci, Oliver Röhrle, Jörg Fehr
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A nonintrusive nonlinear model reduction method for structural dynamical problems based on machine learning
Abstract Model order reduction (MOR) has become one of the most widely used tools to create efficient surrogate models for …
Jonas Kneifl, Dennis Grunert, Jörg Fehr
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