Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations | Paper Citation Record

Observation fdd2b049-85ed-4ff4-bc7f-383a7c65e0d1 · outbound

This paper cites Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Error Analysis of Kernel/GP Methods for Nonlinear and Parametric PDEs

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This paper cites Benner, S.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Benner, S

Reference 2

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This paper cites Benner, M.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Benner, M

Reference 3

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This paper cites Berlinet and C.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Berlinet and C

Reference 4

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This paper cites Binev, A.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Binev, A

Reference 5

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This paper cites Bobrowski, Functional analysis for probability and stochastic processes, Cambridge University Press, Cambridge, 2005.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Bobrowski, Functional analysis for probability and stochastic processes, Cambridge University Press, Cambridge, 2005

Reference 6

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This paper cites Bui-Thanh, K.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Bui-Thanh, K

Reference 7

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This paper cites Chinesta, A.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Chinesta, A

Reference 9

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This paper cites Cohen and R.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Cohen and R

Reference 10

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This paper cites Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Analysis and Design of Convolutional Networks via Hierarchical Tensor Decompositions

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This paper cites On the Expressive Power of Deep Learning: A Tensor Analysis.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations On the Expressive Power of Deep Learning: A Tensor Analysis

Reference 12

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This paper cites Dautray and J.-L.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Dautray and J.-L

Reference 14

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This paper cites Drohmann, B.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Drohmann, B

Reference 15

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Observation 31f8df2f-de6e-48a8-8272-0b5360656373 · outbound

This paper cites Evensen,Data assimilation: The ensemble kalman filter, Springer, Berlin, 2007.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Evensen,Data assimilation: The ensemble kalman filter, Springer, Berlin, 2007

Reference 16

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This paper cites Falcó and A.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Falcó and A

Reference 17

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This paper cites Fleuret,The little book of deep learning, v1.1.1 ed., Université de Genève, 2023, Available from: https://fleuret.org/public/lbdl.pdf.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Fleuret,The little book of deep learning, v1.1.1 ed., Université de Genève, 2023, Available from: https://fleuret.org/public/lbdl.pdf

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:03.840714Z

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This paper cites Best low-rank approximations and Kolmogorov n-widths.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Best low-rank approximations and Kolmogorov n-widths

Reference 19

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Observation 66ed9505-2622-4242-95a3-d4f7e47ee9f8 · outbound

This paper cites Noise Tokens: Learning Neural Noise Templates for Environment-Aware Speech Enhancement.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Noise Tokens: Learning Neural Noise Templates for Environment-Aware Speech Enhancement

Reference 20

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Observation 45fa8faf-1be2-4ac8-946f-f38e776c7017 · outbound

This paper cites Ghanem, D.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Ghanem, D

Reference 21

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This paper cites Giraldi and A.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Giraldi and A

Reference 22

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This paper cites Hackbusch,Tensor spaces and numerical tensor calculus, Springer, Berlin, 2012,doi:10.1007/ 978-3-642-28027-6.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Hackbusch,Tensor spaces and numerical tensor calculus, Springer, Berlin, 2012,doi:10.1007/ 978-3-642-28027-6

Reference 23

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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This paper cites A Theoretical Analysis of Deep Neural Networks and Parametric PDEs.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations A Theoretical Analysis of Deep Neural Networks and Parametric PDEs

Reference 28

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This paper cites Latz,Bayesian inverse problems are usually well-posed, SIAM Review65 (2023), no.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Latz,Bayesian inverse problems are usually well-posed, SIAM Review65 (2023), no

Reference 29

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This paper cites Masson-Delmotte, P.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Masson-Delmotte, P

Reference 30

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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This paper cites Stein, R.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Stein, R

Reference 32

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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This paper cites Bayesian Parameter Estimation via Filtering and Functional Approximations.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Bayesian Parameter Estimation via Filtering and Functional Approximations

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

Reference 36

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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This paper cites Ibrahimbegović, ed.), Computational Methods in Applied Sciences, vol.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Ibrahimbegović, ed.), Computational Methods in Applied Sciences, vol

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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This paper cites Operator Learning Using Random Features: A Tool for Scientific Computing.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Operator Learning Using Random Features: A Tool for Scientific Computing

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This paper cites O’Hagan and M.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations O’Hagan and M

Reference 42

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Observation 2a48b90f-9dd3-4009-87dc-46702c93c870 · outbound

This paper cites Pajonk, B.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Pajonk, B

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Observation 55dcf156-13d5-4356-a499-93b2688ad751 · outbound

This paper cites Pollard,A user’s guide to measure theoretic probability, Cambridge University Press, 2001.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Pollard,A user’s guide to measure theoretic probability, Cambridge University Press, 2001

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:03.658873Z

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Observation 1dbb17df-8cb0-4fb1-ab61-2d8f080273a8 · outbound

This paper cites Quarteroni, A.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Quarteroni, A

Reference 45

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This paper cites Quarteroni and G.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Quarteroni and G

Reference 46

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Observation a56356c9-95a8-4f6e-9047-11105d88d66d · outbound

This paper cites Raissi, P.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Raissi, P

Reference 47

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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This paper cites Deep Learning in Neural Networks: An Overview.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Deep Learning in Neural Networks: An Overview

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Schwab and J

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Observation 319f50fa-f9a0-418e-ab2c-77b35ed71b18 · outbound

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Unresolved cited work

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Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Soize and R

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Resolution: malformed identifier; raw_fallback, observed 2026-08-11T06:00:03.626182Z

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Observation 66579b74-95ef-4a86-b020-07ece0c51b89 · outbound

This paper cites Strang,Linear Algebra and Learning from Data, Wellesley - Cambridge Press, 2019.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Strang,Linear Algebra and Learning from Data, Wellesley - Cambridge Press, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:03.611420Z

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Observation 561dc8f9-a0f4-480b-b22b-ada0a26db3b1 · outbound

This paper cites Vondřejc and H.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Vondřejc and H

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T06:00:03.592470Z

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Observation a241be08-6e9f-41ca-98de-0e7fd731406c · outbound

This paper cites Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations Tensor Programs I: Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes

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Observation 8f40c2b4-541e-4b62-85da-a57b19e58f85 · outbound

This paper cites A Fine-Grained Spectral Perspective on Neural Networks.

Reduced Order Models and Conditional Expectation -- Analysing Parametric Low-Order Approximations A Fine-Grained Spectral Perspective on Neural Networks

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