Imagen de Portada

Efficient reconstruction of multidimensional random field models with heterogeneous data using stochastic neural networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Xia, Mingtao, Shen, Qijing
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Numerical Analysis Probability 60-08, 60A99, 62M45
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks
por: Xia, Mingtao, et al.
Publicado: (2025)

A local squared Wasserstein-2 method for efficient reconstruction of models with uncertainty
por: Xia, Mingtao, et al.
Publicado: (2024)

Statistical inference for a multiscale stochastic model of enzyme kinetics via propagation of chaos
por: Ganguly, Arnab, et al.
Publicado: (2024)

Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
por: Lee, Hoil, et al.
Publicado: (2022)

In almost all shallow analytic neural network optimization landscapes, efficient minimizers have strongly convex neighborhoods
por: Benning, Felix, et al.
Publicado: (2025)

Series representations for the characteristic function of the multidimensional Markov random flight
por: Kolesnik, Alexander D.
Publicado: (2023)

A Robbins--Monro Sequence That Can Exploit Prior Information For Faster Convergence
por: Liu, Siwei, et al.
Publicado: (2024)

An adaptive radial basis function approach for efficiently solving multidimensional spatiotemporal integrodifferential equations
por: Xia, Mingtao, et al.
Publicado: (2026)

On the novel geometric and negative binomial INAR(1) processes
por: Bouzar, Nadjib
Publicado: (2023)

Functional worst risk minimization
por: Kennerberg, Philip, et al.
Publicado: (2024)

Critical Points of Random Neural Networks
por: Di Lillo, Simmaco
Publicado: (2025)

Path-Dependent SDEs: Solutions and Parameter Estimation
por: Semnani, Pardis, et al.
Publicado: (2025)

Restricted Path Characteristic Function Determines the Law of Stochastic Processes
por: Li, Siran, et al.
Publicado: (2024)

An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
por: Xia, Mingtao, et al.
Publicado: (2024)

A new local time-decoupled squared Wasserstein-2 method for training stochastic neural networks to reconstruct uncertain parameters in dynamical systems
por: Xia, Mingtao, et al.
Publicado: (2025)

Convergence rates for a finite volume scheme of the stochastic heat equation
por: Sapountzoglou, Niklas, et al.
Publicado: (2024)

Convergence rates for a finite volume scheme of a stochastic non-linear parabolic equation
por: Rajasekaran, Kavin, et al.
Publicado: (2025)

Study of a TPFA scheme for the stochastic Allen-Cahn problem with constraint through numerical experiments
por: Sapountzoglou, Niklas, et al.
Publicado: (2025)

Fractal and Regular Geometry of Deep Neural Networks
por: Di Lillo, Simmaco, et al.
Publicado: (2025)

Error analysis for empirical risk minimization over clipped ReLU networks in solving linear Kolmogorov partial differential equations
por: Xiao, Jichang, et al.
Publicado: (2023)

A functional Hungarian construction for sums of independent random variables
por: Grama, Ion, et al.
Publicado: (2024)

Sufficientness postulates for measure-valued Pólya urn sequences
por: Sariev, Hristo, et al.
Publicado: (2023)

Orthogonal gamma-based expansion for the CIR's first passage time distribution
por: Di Nardo, Elvira, et al.
Publicado: (2024)

Modeling Unknown Stochastic Dynamical System via Autoencoder
por: Xu, Zhongshu, et al.
Publicado: (2023)

Data-driven Effective Modeling of Multiscale Stochastic Dynamical Systems
por: Chen, Yuan, et al.
Publicado: (2024)

Convergence of a spatial semidiscretization for a three-dimensional stochastic Allen-Cahn equation with multiplicative noise
por: Zhou, Qin, et al.
Publicado: (2024)

Random sampling of contingency tables and partitions: Two practical examples of the Burnside process
por: Diaconis, Persi, et al.
Publicado: (2025)

Probabilistic approximation of fully nonlinear second-order PIDEs with convergence rates for the universal robust limit theorem
por: Jiang, Lianzi, et al.
Publicado: (2025)

On Construction, Properties and Simulation of Haar-Based Multifractional Processes
por: Ayache, Antoine, et al.
Publicado: (2025)

Sparsity vs. Statistical Independence in Adaptive Signal Representations: A Case Study of the Spike Process
por: Benichou, Bertrand, et al.
Publicado: (2001)

High-dimensional stochastic finite volumes using the tensor train format
por: Dubois, Juliette, et al.
Publicado: (2025)

Physical blowups via buffered time change in a mean-field neural network
por: Papadopoulos, Nikolaos, et al.
Publicado: (2025)

Dirichlet-Neumann Averaging: The DNA of Efficient Gaussian Process Simulation
por: Kutri, Robert, et al.
Publicado: (2024)

Cluster size distributions of discrete random fields
por: Cheng, Dan, et al.
Publicado: (2026)

Fourier Neural Network Approximation of Transition Densities in Finance
por: Du, Rong, et al.
Publicado: (2023)

Adaptive deep density approximation for stochastic dynamical systems
por: He, Junjie, et al.
Publicado: (2024)

Sequential discretisation schemes for a class of stochastic differential equations and their application to Bayesian filtering
por: Akyildiz, Deniz, et al.
Publicado: (2022)

Asymptotic Expansions for High-Frequency Option Data
por: Chong, Carsten H., et al.
Publicado: (2023)

Preconditioned Conjugate Gradient methods for the estimation of General Linear Models
por: Foschi, Paolo
Publicado: (2025)

Analysis of an exponential integrator for stochastic PDEs driven by Riesz noise
por: Bréhier, Charles-Edouard, et al.
Publicado: (2026)