Paper Citation Record · LEDGER

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification

As of 14 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 2 inbound Pith citation observations for arXiv:2508.00963.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2508.00963 v1

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measured 51 of 51 reference resolution

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Source: arxiv_reference, observed 2026-07-04T08:19:44.904947Z

Reference resolution

51 of 51 outbound references displayed

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Outbound references

Observation 73bc4aa9-0373-4bce-a362-83f8d86abcb9 · outbound

This paper cites Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data.Archives of computational methods in engineering, 27:105–134, 2020.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Virtual, digital and hybrid twins: a new paradigm in data-based engineering and engineered data.Archives of computational methods in engineering, 27:105–134, 2020

Reference 1

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This paper cites Empowering engineering with data, machine learning and artificial in- telligence: a short introductive review.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Empowering engineering with data, machine learning and artificial in- telligence: a short introductive review

Reference 2

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This paper cites Computational sensing, understanding, and reasoning: an artificial intelligence approach to physics-informed world modeling.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Computational sensing, understanding, and reasoning: an artificial intelligence approach to physics-informed world modeling

Reference 3

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This paper cites Deep learning in computational mechanics: a review.Computational Mechanics, 74(2):281–331, 2024.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Deep learning in computational mechanics: a review.Computational Mechanics, 74(2):281–331, 2024

Reference 4

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This paper cites Physics- informed machine learning.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics- informed machine learning

Reference 5

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This paper cites Physical laws meet machine intelligence: current developments and future directions.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physical laws meet machine intelligence: current developments and future directions

Reference 6

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This paper cites Ai meets physics: a comprehensive survey.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Ai meets physics: a comprehensive survey

Reference 7

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This paper cites Knowledge integration into deep learning in dynamical systems: an overview and taxonomy.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Knowledge integration into deep learning in dynamical systems: an overview and taxonomy

Reference 8

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This paper cites Mathematical models in science and engineering.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Mathematical models in science and engineering

Reference 9

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This paper cites Introducing machine learning: science and technology.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Introducing machine learning: science and technology

Reference 10

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This paper cites Physics-informed machine learning and its structural integrity applications: state of the art.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-informed machine learning and its structural integrity applications: state of the art

Reference 11

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This paper cites Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges

Reference 12

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This paper cites Physics-informed machine learning for structural health monitoring.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-informed machine learning for structural health monitoring

Reference 13

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This paper cites Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-informed machine learning: A comprehensive review on applications in anomaly detection and condition monitoring

Reference 14

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This paper cites Physics-informed machine learning: case studies for weather and climate modelling.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-informed machine learning: case studies for weather and climate modelling

Reference 15

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This paper cites Machine learning for the physics of climate.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Machine learning for the physics of climate

Reference 16

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This paper cites Recent advances and applications of machine learning in experimental solid mechanics: A review.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Recent advances and applications of machine learning in experimental solid mechanics: A review

Reference 17

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification A review of physics-informed machine learning in fluid mechanics

Reference 18

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This paper cites Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-guided, physics-informed, and physics-encoded neural networks and operators in scientific computing: Fluid and solid mechanics

Reference 19

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This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Scientific machine learning through physics–informed neural networks: Where we are and what’s next

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Structure-preserving neural networks

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Thermodynamics-informed graph neural networks

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Neural operator: Learning maps between function spaces with applications to pdes

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Fourier Neural Operator for Parametric Partial Differential Equations

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Nn-euclid: Deep-learning hyperelasticity without stress data

Reference 27

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Discovering plasticity models without stress data

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Automated discovery of generalized standard material models with euclid

Reference 29

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers

Reference 30

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Predicting and explaining nonlinear material response using deep physically guided neural networks with internal variables

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification On the application of physically-guided neural networks with internal variables to continuum problems

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Understanding glioblastoma invasion using physically-guided neural networks with internal variables

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Recent advances on the use of separated representations

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Why and when can deep-but not shallow-networks avoid the curse of dimensionality: a review

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Conceptual and empirical comparison of dimensionality reduction algorithms (pca, kpca, lda, mds, svd, lle, isomap, le, ica, t-sne)

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Feature dimensionality reduction: a review

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Unresolved cited work

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification The homogeneous chaos

Reference 39

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification An introduction to the proper orthogonal decomposition

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification On interpretability and proper latent decomposition of autoencoders

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification An introduction to harmonic analysis

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Unresolved cited work

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Cooley and John W

Reference 44

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Code verification by the method of manufactured solutions

Reference 45

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification PyTorch Documentation

Reference 46

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Adam: A Method for Stochastic Optimization

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Robust nonparametric statistical methods

Reference 48

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Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Self-adaptive loss balanced physics-informed neural networks

Reference 49

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Observation 417d2f30-13ac-4f0a-a974-f3fe83447223 · outbound

This paper cites So (3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification So (3)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:04:50.444519Z

Source-reported events for the cited work

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Observation 294366ab-1e95-4e75-9bcd-6b3f2a9b16cc · outbound

This paper cites Convex neural networks.

Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification Convex neural networks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T06:04:50.429570Z

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Pith citing papers

Observation 4fefd5b8-1ff5-47ff-a862-aaea1c3cf94a · inbound

Attractor Domain Theory: A Mathematical Framework for Cardiovascular Attractor Analysis with Wearable Photoplethysmography (PPG) Validation cites this paper.

Attractor Domain Theory: A Mathematical Framework for Cardiovascular Attractor Analysis with Wearable Photoplethysmography (PPG) Validation Rethinking Multimodality: Optimizing Multimodal Deep Learning for Biomedical Signal Classification

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.906444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8ad2dbd9-1304-4dc2-a881-d906d55fd70e · inbound

Attractor Domain Theory: A Mathematical Framework for Cardiovascular Attractor Analysis with Wearable Photoplethysmography (PPG) Validation cites this paper.

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T10:42:18.628582Z

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