Image de couverture de livre

Stochastic generative methods for stable and accurate closure modeling of chaotic dynamical systems

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Williams, Emily, Darmofal, David
Format:	Preprint
Publié:	2025
Sujets:	Numerical Analysis Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Structure-preserving neural networks for the regularized entropy-based closure of the Boltzmann moment system
par: Schotthöfer, Steffen, et autres
Publié: (2024)

What do physics-informed DeepONets learn? Understanding and improving training for scientific computing applications
par: Williams, Emily, et autres
Publié: (2024)

Coupling the reduced-order model and the generative model for an importance sampling estimator
par: Wan, Xiaoliang, et autres
Publié: (2019)

Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
par: Zhang, Benjamin J., et autres
Publié: (2025)

State-space models are accurate and efficient neural operators for dynamical systems
par: Hu, Zheyuan, et autres
Publié: (2024)

Stable neural networks and connections to continuous dynamical systems
par: Ehrhardt, Matthias J., et autres
Publié: (2025)

Stein's method for marginals on large graphical models
par: Cui, Tiangang, et autres
Publié: (2024)

Wasserstein Bounds for generative diffusion models with Gaussian tail targets
par: Wang, Xixian, et autres
Publié: (2024)

A fast neural hybrid Newton solver adapted to implicit methods for nonlinear dynamics
par: Jin, Tianyu, et autres
Publié: (2024)

A novel data generation scheme for surrogate modelling with deep operator networks
par: Choubey, Shivam, et autres
Publié: (2024)

Leveraging time and parameters for nonlinear model reduction methods
par: Glas, Silke, et autres
Publié: (2025)

On latent dynamics learning in nonlinear reduced order modeling
par: Farenga, Nicola, et autres
Publié: (2024)

A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder
par: Kim, Youngkyu, et autres
Publié: (2020)

Machine learning moment closure models for the radiative transfer equation IV: enforcing symmetrizable hyperbolicity in two dimensions
par: Huang, Juntao
Publié: (2026)

Learning solution operator of dynamical systems with diffusion maps kernel ridge regression
par: Song, Jiwoo, et autres
Publié: (2025)

Flow-based Bayesian filtering for high-dimensional nonlinear stochastic dynamical systems
par: Wang, Xintong, et autres
Publié: (2025)

Training-free score-based diffusion for parameter-dependent stochastic dynamical systems
par: Yang, Minglei, et autres
Publié: (2026)

Energy stable neural network for gradient flow equations
par: Wu, Yue, et autres
Publié: (2023)

Generative AI for fast and accurate statistical computation of fluids
par: Molinaro, Roberto, et autres
Publié: (2024)

Streaming Krylov-Accelerated Stochastic Gradient Descent
par: Thomas, Stephen
Publié: (2025)

Learning Stochastic Dynamical Systems with Structured Noise
par: Guo, Ziheng, et autres
Publié: (2025)

Stochastic diagonal estimation with adaptive parameter selection
par: Han, Zongyuan, et autres
Publié: (2024)

Stochastic Rounding Increases Small Singular Values
par: Ma, Linkai, et autres
Publié: (2026)

Statistical closure modeling for reduced-order models of stationary systems by the ROMES method
par: Pagani, Stefano, et autres
Publié: (2019)

Convergence analysis of OT-Flow for sample generation
par: Jing, Yang, et autres
Publié: (2024)

A model-data asymptotic-preserving neural network method based on micro-macro decomposition for gray radiative transfer equations
par: Li, Hongyan, et autres
Publié: (2022)

Neural Network Approach to Stochastic Dynamics for Smooth Multimodal Density Estimation
par: Zarezadeh, Z., et autres
Publié: (2025)

Training with Fewer Bits: Unlocking Edge LLMs Training with Stochastic Rounding
par: Liu, Taowen, et autres
Publié: (2025)

Parametric model reduction of mean-field and stochastic systems via higher-order action matching
par: Berman, Jules, et autres
Publié: (2024)

Modified Equations for Stochastic Optimization
par: Perko, Stefan
Publié: (2025)

Convergence of Stochastic Gradient Methods for Wide Two-Layer Physics-Informed Neural Networks
par: Jin, Bangti, et autres
Publié: (2025)

An Enhanced Zeroth-Order Stochastic Frank-Wolfe Framework for Constrained Finite-Sum Optimization
par: Ye, Haishan, et autres
Publié: (2025)

Stochastic Dimension Implicit Functional Projections for Exact Integral Conservation in High-Dimensional PINNs
par: Liang, Zhangyong
Publié: (2026)

Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics
par: Santos, Rômulo Damasclin Chaves dos, et autres
Publié: (2025)

Meshless method stencil evaluation with machine learning
par: Rot, Miha, et autres
Publié: (2022)

Learning efficient and provably convergent splitting methods
par: Kreusser, L. M., et autres
Publié: (2024)

High-dimensional multi-view clustering methods
par: Zahir, Alaeddine, et autres
Publié: (2023)

When big data actually are low-rank, or entrywise approximation of certain function-generated matrices
par: Budzinskiy, Stanislav
Publié: (2024)

How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework
par: Ren, Yinuo, et autres
Publié: (2024)

A score-based particle method for homogeneous Landau equation
par: Huang, Yan, et autres
Publié: (2024)