Pages that link to "Item:Q2222510"
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The following pages link to A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the small data regime (Q2222510):
Displaying 12 items.
- Predictive coarse-graining (Q1685164) (← links)
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
- Symplectic neural networks in Taylor series form for Hamiltonian systems (Q2124341) (← links)
- Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems (Q2125437) (← links)
- Enforcing exact physics in scientific machine learning: a data-driven exterior calculus on graphs (Q2133772) (← links)
- A data-driven surrogate to image-based flow simulations in porous media (Q2176870) (← links)
- A novel deep learning-based modelling strategy from image of particles to mechanical properties for granular materials with CNN and BiLSTM (Q2237268) (← links)
- Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems (Q2319398) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Physics-Constrained, Data-Driven Discovery of Coarse-Grained Dynamics (Q5161415) (← links)
- Machine learning meta-models for fast parameter identification of the lattice discrete particle model (Q6164296) (← links)
- Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
