Towards out of distribution generalization for problems in mechanics (Q2083180)

scientific article; zbMATH DE number 7598938

Language	Label	Description	Also known as
English	Towards out of distribution generalization for problems in mechanics	scientific article; zbMATH DE number 7598938

Statements

instance of

scholarly article

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title

Towards out of distribution generalization for problems in mechanics (English)

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Computer Methods in Applied Mechanics and Engineering

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publication date

10 October 2022

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full work available at URL

https://arxiv.org/abs/2206.14917

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zbMATH Keywords

machine learning

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out of distribution

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regression

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covariate shift

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mechanism shift

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sampling bias

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describes a project that uses

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Deformation of FCC nanowires by twinning and slip

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Hierarchical deep learning neural network (HiDeNN): an artificial intelligence (AI) framework for computational science and engineering

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Quantification and compensation of thermal distortion in additive manufacturing: a computational statistics approach

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Application of deep learning neural network to identify collision load conditions based on permanent plastic deformation of shell structures

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Meta-modeling game for deriving theory-consistent, microstructure-based traction-separation laws via deep reinforcement learning

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A cooperative game for automated learning of elasto-plasticity knowledge graphs and models with AI-guided experimentation

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A feed-forwarded neural network-based variational Bayesian learning approach for forensic analysis of traffic accident

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A generalized Bayesian regularization network approach on characterization of geometric defects in lattice structures for topology optimization in preliminary design of 3D printing

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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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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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Scientific machine learning through physics-informed neural networks: where we are and what's next

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems

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Data-driven discovery of coordinates and governing equations

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Causal inference using invariant prediction: identification and confidence intervals

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Causality for Machine Learning

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Invariance, causality and robustness

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Learning models with uniform performance via distributionally robust optimization

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DOLFIN

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Automated solution of differential equations by the finite element method. The FEniCS book

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High–Reynolds Number Wall Turbulence

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.CMA.2022.115569

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Sitelinks

Mathematics(1 entry)

mardi Publication:2083180