Pages that link to "Item:Q2125437"
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The following pages link to Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems (Q2125437):
Displaying 11 items.
- Bridging the gap: machine learning to resolve improperly modeled dynamics (Q2116291) (← links)
- A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables (Q2124009) (← links)
- Numerical analysis of non-local calculus on finite weighted graphs, with application to reduced-order modeling of dynamical systems (Q2679320) (← links)
- Intelligent dissipative particle dynamics: bridging mesoscopic models from microscopic simulations via deep neural networks (Q2683077) (← links)
- Physics-Informed Probabilistic Learning of Linear Embeddings of Nonlinear Dynamics with Guaranteed Stability (Q5109771) (← links)
- Fully probabilistic deep models for forward and inverse problems in parametric PDEs (Q6095115) (← links)
- Semi-supervised invertible neural operators for Bayesian inverse problems (Q6164274) (← links)
- Information theoretic clustering for coarse-grained modeling of non-equilibrium gas dynamics (Q6553796) (← links)
- SDYN-GANs: adversarial learning methods for multistep generative models for general order stochastic dynamics (Q6639347) (← links)
- Physics-aware neural implicit solvers for multiscale, parametric PDEs with applications in heterogeneous media (Q6641874) (← links)
- Weak neural variational inference for solving Bayesian inverse problems \textit{without} forward models: applications in elastography (Q6663307) (← links)
