A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs | Paper Citation Record

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This paper cites Foti, and Emily B.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Foti, and Emily B

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This paper cites A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs A fully adaptive nonintrusive reduced-order modelling approach for parametrized time-dependent problems

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This paper cites Ascher and Linda R.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Ascher and Linda R

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Layer Normalization

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This paper cites Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Representation Equivalent Neural Operators: a Framework for Alias-free Operator Learning

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This paper cites Representation equivalent neural operators: a framework for alias-free operator learning.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Representation equivalent neural operators: a framework for alias-free operator learning

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This paper cites Model reduction and neural networks for parametric pdes.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Model reduction and neural networks for parametric pdes

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This paper cites Message Passing Neural PDE Solvers.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Message Passing Neural PDE Solvers

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This paper cites Brunton, Joshua L.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Brunton, Joshua L

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Unresolved cited work

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This paper cites Choose a transformer: Fourier or galerkin.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Choose a transformer: Fourier or galerkin

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This paper cites Neural ordinary differential equations.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Neural ordinary differential equations

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This paper cites Learning the irreducible representations of commutative lie groups.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Learning the irreducible representations of commutative lie groups

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This paper cites A guide to convolution arithmetic for deep learning, 2016.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs A guide to convolution arithmetic for deep learning, 2016

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Unresolved cited work

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This paper cites Multi-Scale Message Passing Neural PDE Solvers.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Multi-Scale Message Passing Neural PDE Solvers

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This paper cites Testing the manifold hypothesis.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Testing the manifold hypothesis

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This paper cites Deep learning-based surrogate models for parametrized pdes: Handling geometric variability through graph neural networks.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Deep learning-based surrogate models for parametrized pdes: Handling geometric variability through graph neural networks

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This paper cites Pod-dl-rom: Enhancing deep learning-based reduced order models for nonlinear parametrized pdes by proper orthogonal decomposition.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Pod-dl-rom: Enhancing deep learning-based reduced order models for nonlinear parametrized pdes by proper orthogonal decomposition

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This paper cites Pooling Methods in Deep Neural Networks, a Review.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Pooling Methods in Deep Neural Networks, a Review

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This paper cites Modeling the influence of data structure on learning in neural networks: The hidden manifold model.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Modeling the influence of data structure on learning in neural networks: The hidden manifold model

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This paper cites Towards Multi-spatiotemporal-scale Generalized PDE Modeling.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Towards Multi-spatiotemporal-scale Generalized PDE Modeling

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This paper cites Vectorized Conditional Neural Fields: A framework for solving time-dependent parametric partial differential equations.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Vectorized Conditional Neural Fields: A framework for solving time-dependent parametric partial differential equations

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This paper cites Gnot: A general neural operator transformer for operator learning.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Gnot: A general neural operator transformer for operator learning

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This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

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This paper cites Gaussian Error Linear Units (GELUs).

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Gaussian Error Linear Units (GELUs)

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Towards a Definition of Disentangled Representations

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs An investigation of uncertainty and sensitivity analysis techniques for computer-models

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Mionet: Learning multiple-input operators via tensor product

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs On Neural Differential Equations

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Adam: A Method for Stochastic Optimization

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Learning operators with coupled attention

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Space-Time Continuous PDE Forecasting using Equivariant Neural Fields

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Neural Operator: Learning Maps Between Function Spaces

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Koopman Theory for Partial Differential Equations

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Unresolved cited work

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Carlberg

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Transformer for partial differential equations’ operator learning

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Fourier Neural Operator for Parametric Partial Differential Equations

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Geometry-informed neural operator for large-scale 3d pdes

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

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data

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This paper cites Nathan Kutz, and Steven L.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Nathan Kutz, and Steven L

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Film: Visual reasoning with a general conditioning layer

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Grid and basis adaptive polynomial chaos techniques for sensitivity and uncertainty analysis

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This paper cites A graph convolutional autoencoder approach to model order reduction for parametrized PDEs.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs A graph convolutional autoencoder approach to model order reduction for parametrized PDEs

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This paper cites Reduced Order Methods for Modeling and Computational Reduction.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Reduced Order Methods for Modeling and Computational Reduction

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Numerical Mathematics (Texts in Applied Mathematics)

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This paper cites Raissi, P.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Raissi, P

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This paper cites U-net: Convolutional networks for biomedical image segmentation.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs U-net: Convolutional networks for biomedical image segmentation

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This paper cites Wang, Yuan Yin, Jean-No¨ el Vittaut, and Patrick Gallinari.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Wang, Yuan Yin, Jean-No¨ el Vittaut, and Patrick Gallinari

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A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Unresolved cited work

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This paper cites Learning neural pde solvers with parameter-guided channel attention.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Learning neural pde solvers with parameter-guided channel attention

Reference 55

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Observation 8b7c3640-41b6-4792-892b-fcde971a67bf · outbound

This paper cites Pdebench: An extensive benchmark for scientific machine learning.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Pdebench: An extensive benchmark for scientific machine learning

Reference 56

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Observation 7d78448a-6d0c-4343-8a3c-042eea8bcaa7 · outbound

This paper cites Neural fields in visual computing and beyond.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Neural fields in visual computing and beyond

Reference 57

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Observation 461e9348-73ae-479c-bc7a-a9b11181677f · outbound

This paper cites Continuous PDE dynamics forecasting with implicit neural representations.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Continuous PDE dynamics forecasting with implicit neural representations

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:46:28.812034Z

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Observation 8a19704b-978b-4140-a4f1-5afa64d59e23 · outbound

This paper cites Inference time of other methods is from [23] where they use an NVIDIA A100-SXM4 80GB GPU.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs Inference time of other methods is from [23] where they use an NVIDIA A100-SXM4 80GB GPU

Reference 64

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T05:46:28.523078Z

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Observation 47b7b048-4e27-4255-9aa4-78c36eafe98c · outbound

This paper cites fθ is composed by 4 hidden layers with 200 neurons each and λ = 30.

A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs fθ is composed by 4 hidden layers with 200 neurons each and λ = 30

Reference 124

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:46:28.796058Z

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