Probabilistic partition of unity networks for high‐dimensional regression problems (Q6062830)

scientific article; zbMATH DE number 7773168

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English	Probabilistic partition of unity networks for high‐dimensional regression problems	scientific article; zbMATH DE number 7773168

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instance of

scholarly article

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title

Probabilistic partition of unity networks for high‐dimensional regression problems (English)

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International Journal for Numerical Methods in Engineering

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publication date

2 December 2023

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full work available at URL

https://arxiv.org/abs/2210.02694

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zbMATH Keywords

deep learning

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dimensionality reduction

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mixture of experts

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nonparametric regression

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MaRDI profile type

MaRDI publication profile

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cites work

Error bounds for approximations with deep ReLU networks

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Q4843404

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Breaking the Curse of Dimensionality with Convex Neural Networks

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Q2781419

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Radial Basis Functions

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Q4488070

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Projection-based approximation and a duality with kernel methods

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A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data

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Lebesgue functions and Lebesgue constants in polynomial interpolation

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Solving high-dimensional partial differential equations using deep learning

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

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Learning constitutive relations from indirect observations using deep neural networks

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Learning generative neural networks with physics knowledge

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Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification

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Deep learning in high dimension: Neural network expression rates for generalized polynomial chaos expansions in UQ

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A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

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Active Subspaces

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Exploiting active subspaces to quantify uncertainty in the numerical simulation of the hyshot II scramjet

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Kernel PCA for novelty detection

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EXTENDING CLASSICAL SURROGATE MODELING TO HIGH DIMENSIONS THROUGH SUPERVISED DIMENSIONALITY REDUCTION: A DATA-DRIVEN APPROACH

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Non-intrusive low-rank separated approximation of high-dimensional stochastic models

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Spectral Tensor-Train Decomposition

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Gradient-based optimization for regression in the functional tensor-train format

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Reducing the Dimensionality of Data with Neural Networks

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Solving parametric PDE problems with artificial neural networks

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

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

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Identifiers

arXiv ID

2210.02694

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Mathematics Subject Classification ID

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Mathematics(1 entry)

mardi Publication:6062830