A framework for strategic discovery of credible neural network surrogate models under uncertainty (Q6557831)

scientific article; zbMATH DE number 7867445

Language	Label	Description	Also known as
English	A framework for strategic discovery of credible neural network surrogate models under uncertainty	scientific article; zbMATH DE number 7867445

Statements

instance of

scholarly article

0 references

title

A framework for strategic discovery of credible neural network surrogate models under uncertainty (English)

0 references

author

Pratyush Kumar Singh

0 references

Kathryn Farrell-Maupin

0 references

Danial Faghihi

0 references

published in

Computer Methods in Applied Mechanics and Engineering

0 references

publication date

18 June 2024

0 references

zbMATH Keywords

Bayesian neural networks

0 references

surrogate modeling

0 references

model validation

0 references

uncertainty quantification

0 references

model plausibility

0 references

MaRDI profile type

Publication

0 references

cites work

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

0 references

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

0 references

Goal-oriented adaptive modeling of random heterogeneous media and model-based multilevel Monte Carlo methods

0 references

Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks

0 references

Bayesian model calibration for diblock copolymer thin film self-assembly using power spectrum of microscopy data and machine learning surrogate

0 references

A scalable framework for multi-objective PDE-constrained design of building insulation under uncertainty

0 references

Optimal control of PDEs using physics-informed neural networks

0 references

A digital-twin and machine-learning framework for precise heat and energy management of data-centers

0 references

Large-scale Bayesian optimal experimental design with derivative-informed projected neural network

0 references

Towards out of distribution generalization for problems in mechanics

0 references

A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria

0 references

Toward selecting optimal predictive multiscale models

0 references

Probable networks and plausible predictions — a review of practical Bayesian methods for supervised neural networks

0 references

Q4633077

0 references

A predictive multiphase model of silica aerogels for building envelope insulations

0 references

SELECTION AND ASSESSMENT OF PHENOMENOLOGICAL MODELS OF TUMOR GROWTH

0 references

Bayesian-based predictions of COVID-19 evolution in Texas using multispecies mixture-theoretic continuum models

0 references

A computational framework for dynamic data-driven material damage control, based on Bayesian inference and model selection

0 references

A Bayesian framework for adaptive selection, calibration, and validation of coarse-grained models of atomistic systems

0 references

Toward predictive multiscale modeling of vascular tumor growth, computational and experimental oncology for tumor prediction

0 references

Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons

0 references

B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

0 references

Bayesian neural networks for uncertainty quantification in data-driven materials modeling

0 references

Bayesian physics informed neural networks for real-world nonlinear dynamical systems

0 references

Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets

0 references

On the influence of over-parameterization in manifold based surrogates and deep neural operators

0 references

Multi-fidelity Bayesian neural networks: algorithms and applications

0 references

Biomimetic IGA neuron growth modeling with neurite morphometric features and CNN-based prediction

0 references

Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation

0 references

A forward-backward greedy approach for sparse multiscale learning

0 references

Sparse methods for automatic relevance determination

0 references

Probability Theory

0 references

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

0 references

Residual-based error corrector operator to enhance accuracy and reliability of neural operator surrogates of nonlinear variational boundary-value problems

0 references

Isogeometric analysis-based physics-informed graph neural network for studying traffic jam in neurons

0 references

Learning high-dimensional parametric maps via reduced basis adaptive residual networks

0 references

Derivative-informed projected neural networks for high-dimensional parametric maps governed by PDEs

0 references

Identifiers

DOI

10.1016/j.cma.2024.117061

0 references

Mathematics Subject Classification ID

0 references

0 references

0 references

0 references

0 references

0 references

0 references

Sitelinks

Mathematics(1 entry)

mardi Publication:6557831