Distribution free uncertainty quantification in neuroscience-inspired deep operators | Paper Citation Record

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This paper cites Machine learning: Trends, perspectives, and prospects.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Machine learning: Trends, perspectives, and prospects

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Machine learning algorithms-a review

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Machine learning

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Reliability-based design optimization using kriging surrogates and subset simulation

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Support vector machine in structural reliability analysis: A review.Reliability Engineering & System Safety, 233:109126, 2023

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Reliability analyses of underground tunnels by an adaptive support vector regression model

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This paper cites Explainable, interpretable, and trustworthy ai for an intelli- gent digital twin: A case study on remaining useful life.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Explainable, interpretable, and trustworthy ai for an intelli- gent digital twin: A case study on remaining useful life

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Deep neural operator-driven real-time inference to enable digital twin solutions for nuclear energy systems

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Advances in computational intelligence of polymer composite materials: machine learning assisted mod- eling, analysis and design

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Energy-based physics-informed neural network for frictionless contact problems under large deformation

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This paper cites Artificial intelligence for partial differential equa- tions in computational mechanics: A review.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Artificial intelligence for partial differential equa- tions in computational mechanics: A review

Reference 12

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Distribution free uncertainty quantification in neuroscience-inspired deep operators A novel machine-learning framework with a moving platform for maritime drift calculations

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Accelerated neural network solvers of navier stokes equations for turbulent flows

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Scientific machine learning through physics–informed neural networks: Where we are and what’s next

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Scientific machine learning bench- marks

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Machine learning and big scientific data

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Distribution free uncertainty quantification in neuroscience-inspired deep operators An introduction to neural networks

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Deep learning

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Distribution free uncertainty quantification in neuroscience-inspired deep operators State-of-the-art in artificial neural network applications: A survey

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This paper cites Learning nonlinear op- erators via deeponet based on the universal approximation theorem of operators.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Learning nonlinear op- erators via deeponet based on the universal approximation theorem of operators

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Fourier Neural Operator for Parametric Partial Differential Equations

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Wavelet neural operator for solving parametric partial differential equations in computational mechanics problems

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Neural operator: Learning maps between function spaces with applications to pdes

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Introduction to finite element methods

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Distribution free uncertainty quantification in neuroscience-inspired deep operators The finite element method in engineering

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Neuroscience inspired neural operator for partial differential equations

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Spiking Neural Operators for Scientific Machine Learning

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Conformal prediction: A gentle introduction

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Distribution-free predictive inference for regression

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Conformalized-DeepONet: A Distribution-Free Framework for Uncertainty Quantification in Deep Operator Networks

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Calibrated Uncertainty Quantification for Operator Learning via Conformal Prediction

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Hands- on bayesian neural networks—a tutorial for deep learning users

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This paper cites What are bayesian neural network posteriors really like? In International conference on machine learning , pages 4629–4640.

Distribution free uncertainty quantification in neuroscience-inspired deep operators What are bayesian neural network posteriors really like? In International conference on machine learning , pages 4629–4640

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Weight uncertainty in neural net- work

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Distribution free uncertainty quantification in neuroscience-inspired deep operators A tutorial on conformal prediction

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Randomized prior functions for deep reinforcement learning

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Randomized prior wavelet neural operator for uncertainty quantification

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Gaussian processes for machine learning , volume 2

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Distribution free uncertainty quantification in neuroscience-inspired deep operators A tutorial on gaussian process regression: Modelling, exploring, and exploiting functions

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Quantile regression

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Quantile regression

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This paper cites Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression

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This paper cites Surrogate gradient learning in spiking neural net- works: Bringing the power of gradient-based optimization to spiking neural networks.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Surrogate gradient learning in spiking neural net- works: Bringing the power of gradient-based optimization to spiking neural networks

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This paper cites Graph-theoretic-approach-assisted gaussian process for nonlinear stochastic dynamic analysis under generalized loading.

Distribution free uncertainty quantification in neuroscience-inspired deep operators Graph-theoretic-approach-assisted gaussian process for nonlinear stochastic dynamic analysis under generalized loading

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This paper cites A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data.

Distribution free uncertainty quantification in neuroscience-inspired deep operators A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data

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Distribution free uncertainty quantification in neuroscience-inspired deep operators Openfwi: Large-scale multi-structural benchmark datasets for full waveform inversion

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