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Source: paper_references, paper_reference_links, observed 2026-07-14T08:02:00.306457Z
Paper Citation Record · LEDGER
As of 9 August 2026, this Paper Citation Record lists 59 of 59 outbound references and 0 inbound Pith citation observations for arXiv:2607.10965.
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Source: paper_references, paper_reference_links, observed 2026-07-14T08:02:00.306457Z
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
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59 of 59 outbound references displayed
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Observation 21fe54c1-e3d0-4d2c-ae7f-8a743d7c22e4 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Turbulence and the dynamics of coherent structures part III: Dy- namics and scaling
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Observation dd6055c1-71c0-4a49-b739-03ec67647c74 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Lumley, and Emily Stone
Reference 2
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Observation 19d12f93-8efc-4b41-980f-129b6c72f800 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws A survey of projection-based model reduction methods for parametric dynamical systems
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Observation 226be14e-9edb-4c60-8d3f-873ebda2a4b8 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
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Observation 78b89306-ad95-486c-b757-3f28b6ee544e · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Adjacency-based, non-intrusive model reduction for vortex-induced vibrations
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
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Observation 5fa9d5e2-e953-45b9-83fa-b844155194b7 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Fourier Neural Operator for Parametric Partial Differential Equations
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Learning nonlinear operators via DeepONet based on the universal approximation the- orem of operators
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Fries, Xiaolong He, and Youngsoo Choi
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
Reference 12
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Observation 45eb7b0d-34ae-4160-8ac6-1c62129eb7c0 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
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Observation fde5806b-1f13-4da4-919d-f1fe90c20b84 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Southworth, and Marc L
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators
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Observation 57b3a184-0bec-4fe8-8186-5510a58f226d · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Plasma surrogate modelling using fourier neural opera- tors
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws A fast and ac- curate physics-informed neural network reduced order model with shallow masked au- toencoder
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Observation fdd9290e-e7d1-4c56-a43c-4e703ad25702 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Chen, Jinxu Xiang, Dong H
Reference 19
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Observation e6ee91b8-881a-4b9b-b5ea-669577640c80 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Kara, and Yongjie J
Reference 20
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Keo Springer, and Kyle T
Reference 21
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Aslam, and Marc L
Reference 22
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Reduced-order modeling for parameterized PDEs via implicit neural representations
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws tlasdi: Thermodynamics-informed latent space dynamics identification
Reference 24
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
Reference 25
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Approximate bayesian neural operators: Uncertainty quantification for para- metric PDEs
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws B-deeponet: An enhanced bayesian deeponet for solving noisy parametric pdes using accelerated replica exchange sgld
Reference 27
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Randomized prior wavelet neural operator for uncertainty quantification
Reference 28
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Observation d4c73f04-fbc0-442b-963a-8e461f5540aa · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Psaros, and George E
Reference 29
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Observation 51907f9f-36df-4efa-b661-7da5c0dd281d · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Maddix, Shima Alizadeh, Gaurav Gupta, Andrew Stuart, Michael W
Reference 30
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Observation 2017b4c5-6966-4393-94f6-a93675e998cd · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Scalable Uncertainty Quantification for Deep Operator Networks using Randomized Priors
Reference 31
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Observation ee4bf4cb-abcb-4747-8bf8-b10d0fe463e6 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Calibrated uncertainty quantification for operator learning via conformal prediction
Reference 32
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Conformalized-deeponet: A distribution-free framework for uncertainty quantification in deep operator networks
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Observation 725527f0-de50-40ad-af85-2793a1bc9e6b · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Probabilistic neural operators for functional uncertainty quantification
Reference 34
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Neural operator induced gaus- sian process framework for probabilistic solution of parametric partial differential equa- tions
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Towards gaussian process for operator learning: An uncertainty aware resolution independent operator learning al- gorithm for computational mechanics
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Operator learning with gaussian processes
Reference 37
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Observation b9a901cf-f0a4-4246-acff-167dcb13d55c · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
Reference 38
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Observation 7cc97f80-d28c-40d1-ac91-02540e61be86 · outbound
Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Nature Communications, 15:10416, 2024
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Kingma and Max Welling
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Learning in latent spaces improves the predictive accuracy of deep neural operators
Reference 41
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Approximation by superpositions of a sigmoidal function
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Multilayer feedforward net- works are universal approximators
Reference 43
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Ln3diff++: Scalable latent neural fields diffusion for speedy 3d generation
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws beta-V AE: Learning basic visual concepts with a constrained variational framework
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Unresolved cited work
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Reference 53
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Adam: A Method for Stochastic Optimization
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Decoupled Weight Decay Regularization
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Structure-preserving variational neural fields: Uncertainty-quantified reduced-order modeling of nonlinear conservation laws Efficient implementation of weighted ENO schemes
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Observation d30b2f9f-60ad-45f2-bca9-d93340437dae · outbound
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Reference 59
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