Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:52:50.460589Z
Paper Citation Record · LEDGER
As of 18 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 2 inbound Pith citation observations for arXiv:2505.08740.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:52:50.460589Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-06-28T13:35:13.585468Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T00:06:24.625148Z
44 of 44 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 57f24423-acf3-48b1-9668-8f22410e555a · outbound
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations write newline
Reference 1
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations A differentiable, physics-informed ecosystem modeling and learning framework for large-scale inverse problems: demonstration with photosynthesis simulations
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Managing computational complexity using surrogate models: a critical review
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations A surrogate-model-based method for constrained optimization
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Unresolved cited work
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Spherical Fourier neural operators: Learning stable dynamics on the sphere
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Unresolved cited work
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Sobolev training for neural networks
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Differentiable, learnable, regionalized process-based models with multiphysical outputs can approach state-of-the-art hydrologic prediction accuracy
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Reference 10
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Model-parallel fourier neural operators as learned surrogates for large-scale parametric pdes
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Multiwavelet-based Operator Learning for Differential Equations
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Shah, and Will Tebbutt
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Unresolved cited work
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations NSFnets ( Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang
Reference 17
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Neural operator: Learning maps between function spaces
Reference 18
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Fourier Neural Operator for Parametric Partial Differential Equations , May 2021
Reference 19
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Physics-informed neural operator for learning partial differential equations, 2023
Reference 20
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Physics- Informed Neural Operator for Learning Partial Differential Equations
Reference 21
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Auxiliary Tasks in Multi-task Learning
Reference 22
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Gradient-enhanced neural network response surface approximations
Reference 23
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Discretize- Optimize vs
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Differential equations, mechanics, and computation, volume 51
Reference 25
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Reference 26
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations On the usefulness of overparameterized ecological models
Reference 27
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Observation 06f7fbd0-6088-41be-804d-091187b6a40b · outbound
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations PhyCRNet : Physics-informed convolutional-recurrent network for solving spatiotemporal PDEs
Reference 28
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Observation 9530b632-19e1-4685-b0db-6afc9c747517 · outbound
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices
Reference 29
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Observation 2870b756-7f3a-4406-8d9d-29734e20f526 · outbound
Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations JAX MD : A Framework for Differentiable Physics
Reference 30
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Appling, Pierre Gentine, Toshiyuki Bandai, Hoshin Gupta, Alexandre Tartakovsky, Marco Baity-Jesi , Fabrizio Fenicia, Daniel Kifer, Li Li, Xiaofeng Liu, Wei Ren, Yi Zheng, Ciaran J
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Improving physics-informed, differentiable hydrologic models for capturing unseen extreme events
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Unresolved cited work
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Wavelet Neural Operator for solving parametric partial differential equations in computational mechanics problems
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Fully probabilistic deep models for forward and inverse problems in parametric pdes
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Learning the solution operator of parametric partial differential equations with physics-informed deeponets
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Brenowitz, Spencer K
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Differentiable Modeling and Optimization of Battery Electrolyte Mixtures Using Geometric Deep Learning , November 2023
Reference 41
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Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations Unresolved cited work
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Estimating Parameter Fields in Multi-Physics PDEs from Scarce Measurements Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
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Spectral Audit of In-Context Operator Networks Sensitivity-Constrained Fourier Neural Operators for Forward and Inverse Problems in Parametric Differential Equations
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