Adaptive deep density approximation for stochastic dynamical systems (Q6671865)

scientific article; zbMATH DE number 7975330

Language	Label	Description	Also known as
English	Adaptive deep density approximation for stochastic dynamical systems	scientific article; zbMATH DE number 7975330

Statements

instance of

scholarly article

0 references

title

Adaptive deep density approximation for stochastic dynamical systems (English)

0 references

0 references

0 references

0 references

Journal of Scientific Computing

0 references

publication date

27 January 2025

0 references

zbMATH Keywords

stochastic dynamical systems

0 references

continuity equation

0 references

deep neural networks

0 references

normalizing flows

0 references

MaRDI profile type

Publication

0 references

cites work

Data-driven closures for stochastic dynamical systems

0 references

From Knothe's Transport to Brenier's Map and a Continuation Method for Optimal Transport

0 references

Deep neural network modeling of unknown partial differential equations in nodal space

0 references

Adaptive Discontinuous Galerkin Method for Response-Excitation PDF Equations

0 references

Numerical methods for high-dimensional probability density function equations

0 references

Local extreme learning machines and domain decomposition for solving linear and nonlinear partial differential equations

0 references

A proposal on machine learning via dynamical systems

0 references

The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems

0 references

Finite Elements and Fast Iterative Solvers

0 references

Solving Time Dependent Fokker-Planck Equations via Temporal Normalizing Flow

0 references

PhyGeoNet: physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular domain

0 references

Combining machine learning and domain decomposition methods for the solution of partial differential equations—A review

0 references

Nonparametric and semiparametric models.

0 references

Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

0 references

\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

0 references

Stochastic Dynamics of Structures

0 references

An Introduction to Computational Stochastic PDEs

0 references

Long-time uncertainty propagation using generalized polynomial chaos and flow map composition

0 references

PPINN: parareal physics-informed neural network for time-dependent PDEs

0 references

Numerical Solution of Partial Differential Equations

0 references

Q4998956

0 references

Q5396710

0 references

A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions

0 references

Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

0 references

The Fokker-Planck equation. Methods of solutions and applications.

0 references

Multivariate Density Estimation

0 references

PFNN: a penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries

0 references

DGM: a deep learning algorithm for solving partial differential equations

0 references

Q3998788

0 references

Adaptive deep density approximation for Fokker-Planck equations

0 references

DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations

0 references

Optimal Transport

0 references

An adaptive multi-element generalized polynomial chaos method for stochastic differential equations

0 references

Long-time integration of parametric evolution equations with physics-informed DeepONets

0 references

On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks

0 references

Deep multiscale model learning

0 references

A mesh-free method for interface problems using the deep learning approach

0 references

Data-driven deep learning of partial differential equations in modal space

0 references

Domain-decomposed Bayesian inversion based on local Karhunen-Lo\`{e}ve expansions

0 references

Weak adversarial networks for high-dimensional partial differential equations

0 references

Approximation capabilities of measure-preserving neural networks

0 references

Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification

0 references

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

0 references

Identifiers

DOI

10.1007/s10915-025-02791-7

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6671865