Pages that link to "Item:Q2456690"
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The following pages link to Stochastic upscaling in solid mechanics: an excercise in machine learning (Q2456690):
Displaying 10 items.
- Generalized variability response functions for two-dimensional elasticity problems (Q459079) (← links)
- A multi-resolution, non-parametric, Bayesian framework for identification of spatially-varying model parameters (Q843476) (← links)
- Probabilistic equivalence and stochastic model reduction in multiscale analysis (Q995316) (← links)
- Response classification of simple polycrystalline microstructures (Q1013914) (← links)
- An FFT method for the computation of thermal diffusivity of porous periodic media (Q1703715) (← links)
- Smart finite elements: a novel machine learning application (Q1986797) (← links)
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
- Machine learning for accelerating macroscopic parameters prediction for poroelasticity problem in stochastic media (Q2226818) (← links)
- What Machine Learning Can Do for Computational Solid Mechanics (Q5051038) (← links)
- A machine learning-based probabilistic computational framework for uncertainty quantification of actuation of clustered tensegrity structures (Q6164273) (← links)
