Pages that link to "Item:Q1723001"
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The following pages link to A multidimensional data-driven sparse identification technique: the sparse proper generalized decomposition (Q1723001):
Displaying 12 items.
- Big data in experimental mechanics and model order reduction: today's challenges and tomorrow's opportunities (Q1639599) (← links)
- Machine learning materials physics: multi-resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures (Q2020954) (← links)
- Surrogate parametric metamodel based on optimal transport (Q2076723) (← links)
- Weakly-invasive Latin-PGD for solving time-dependent non-linear parametrized problems in solid mechanics (Q2156761) (← links)
- Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction (Q2281470) (← links)
- Learning slosh dynamics by means of data (Q2319406) (← links)
- Parametric stress field solutions for heterogeneous materials using proper generalized decomposition (Q2683330) (← links)
- Digital twins that learn and correct themselves (Q6090723) (← links)
- Real-time interaction of virtual and physical objects in mixed reality applications (Q6553513) (← links)
- Parametric uncertainty quantification using proper generalized decomposition applied to neutron diffusion (Q6554175) (← links)
- Physics-based active learning for design space exploration and surrogate construction for multiparametric optimization (Q6593783) (← links)
- Modular parametric PGD enabling online solution of partial differential equations (Q6663424) (← links)
