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Paper Citation Record · LEDGER

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling

As of 8 August 2026, this Paper Citation Record lists 100 of 115 outbound references and 1 inbound Pith citation observation for arXiv:2603.02645.

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2603.02645 v2

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measured 100 of 115 reference resolution

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100 of 115 outbound references displayed

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Outbound references

Observation 34e1f58e-e11c-4fe3-842b-b5dde3a86ba5 · outbound

This paper cites an unresolved cited work.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

Reference 1

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Observation 8e30802e-1e78-4872-87b4-882464cbd075 · outbound

This paper cites Model selection techniques: An overview.IEEE Signal Processing Magazine, 35(6):16–34, 2018.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Model selection techniques: An overview.IEEE Signal Processing Magazine, 35(6):16–34, 2018

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Observation 8f95d479-1542-48e8-a863-e9aa7b1d27be · outbound

This paper cites A review on data-driven constitutive laws for solids.Archives of Computational Methods in Engineering, 32(3):1841–1883, 2025.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling A review on data-driven constitutive laws for solids.Archives of Computational Methods in Engineering, 32(3):1841–1883, 2025

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Observation 32f86f84-65df-4868-9599-f1febf503e19 · outbound

This paper cites Finite electro-elasticity with physics-augmented neural networks.Computer Methods in Applied Mechanics and Engineering, 400:115501, 2022.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Finite electro-elasticity with physics-augmented neural networks.Computer Methods in Applied Mechanics and Engineering, 400:115501, 2022

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Observation 992b323f-e589-4033-92c3-a5191e648c3f · outbound

This paper cites Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal vari- ables.Computational Mechanics, 74(6):1279–1301, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Viscoelasticty with physics-augmented neural networks: model formulation and training methods without prescribed internal vari- ables.Computational Mechanics, 74(6):1279–1301, 2024

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Observation 135f569e-54b0-48fd-91f0-ee1170388228 · outbound

This paper cites Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.Computer Methods in Applied Mechanics and Engineering, 426:116973, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics.Computer Methods in Applied Mechanics and Engineering, 426:116973, 2024

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

Reference 7

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Observation 00d7ba7f-c1d1-4379-899f-b89a404f8de5 · outbound

This paper cites Experimental learning of a hypere- lastic behavior with a physics-augmented neural network.Experimental Mechanics, 64(9):1465–1481, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Experimental learning of a hypere- lastic behavior with a physics-augmented neural network.Experimental Mechanics, 64(9):1465–1481, 2024

Reference 8

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Observation aba5f2d8-aed9-476d-b773-bbfacad2ad20 · outbound

This paper cites Inverse design of anisotropic microstructures using physics-augmented neural networks.Journal of the Mechanics and Physics of Solids, page 106161, 2025.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Inverse design of anisotropic microstructures using physics-augmented neural networks.Journal of the Mechanics and Physics of Solids, page 106161, 2025

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Observation a865595a-5968-4510-93d4-f6958bb9f4f9 · outbound

This paper cites Polyconvex physics- augmented neural network constitutive models in principal stretches.International Journal of Solids and Struc- tures, page 113469, 2025.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Polyconvex physics- augmented neural network constitutive models in principal stretches.International Journal of Solids and Struc- tures, page 113469, 2025

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Observation aed63338-f072-479d-8efa-293c8bfe0b86 · outbound

This paper cites A physics-augmented neural network framework for finite strain incompressible viscoelasticity.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling A physics-augmented neural network framework for finite strain incompressible viscoelasticity

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Observation da9f2e32-80a7-4d6c-8fdc-6b52f10b9717 · outbound

This paper cites Recovering mullins damage hyperelastic behaviour with physics augmented neural networks.Journal of the Mechanics and Physics of Solids, 193:105839, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Recovering mullins damage hyperelastic behaviour with physics augmented neural networks.Journal of the Mechanics and Physics of Solids, 193:105839, 2024

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

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Observation 8cbdf96a-f7e6-4be4-b4ac-43f73d6c9eb3 · outbound

This paper cites A new family of constitutive artificial neural networks towards automated model discovery.Computer Methods in Applied Mechanics and Engineering, 403:115731, 2023.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling A new family of constitutive artificial neural networks towards automated model discovery.Computer Methods in Applied Mechanics and Engineering, 403:115731, 2023

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Observation b8b82815-72b2-45ff-90af-4568b8408fd0 · outbound

This paper cites Theory and implementation of inelastic constitutive artificial neural networks.Computer Methods in Applied Mechanics and Engineering, 428:117063, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Theory and implementation of inelastic constitutive artificial neural networks.Computer Methods in Applied Mechanics and Engineering, 428:117063, 2024

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Observation 4cdb03e1-125a-4b94-90ee-55186c38c02d · outbound

This paper cites Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Accounting for plasticity: An extension of inelastic Constitutive Artificial Neural Networks

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Observation b33465a4-bf68-49cf-ba9f-f6f0cbb6026f · outbound

This paper cites Polyconvex inelastic constitutive artificial neural networks.PAMM, 24(3):e202400032, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Polyconvex inelastic constitutive artificial neural networks.PAMM, 24(3):e202400032, 2024

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

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Observation 32bfa5d9-e136-4594-93ac-1d1e77fca6b9 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

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Observation e237470d-e187-4c03-af91-f3401a4aa4ce · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Hamiltonian neural networks.Advances in neural information processing systems, 32, 2019

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Observation 355aceaf-33af-4e7d-b6bf-b0d4345d23bd · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Lagrangian Neural Networks

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Symplectic Recurrent Neural Networks

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Observation e1a2bdba-4673-49bc-b2a0-2b952ebc8825 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Thermodynamically consistent data-driven computa- tional mechanics.Continuum Mechanics and Thermodynamics, 31(1):239–253, 2019

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Observation 0e8a5742-7c6d-4d5d-aa3d-7ae9e8c76ca6 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Efficiently parameterized neural metriplectic systems

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Observation 8e9d6096-31c1-4d00-b369-75a54f2fe70c · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Thermodynamics with internal state variables.The journal of chemical physics, 47(2):597–613, 1967

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Observation e28de63d-ba81-4a1c-a31b-595a26be36f0 · outbound

This paper cites Cambridge university press, 1994.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Cambridge university press, 1994

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Observation 7e5b67a2-874c-4cd2-806e-a262068645f0 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Sur les matériaux standard généralisés.Journal de mécanique, 14(1):39–63, 1975

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source=pdf_text observed=2026-08-02T19:22:12.938605Z digest=sha256:000ff6e50e193208768f589b591d4da3d3cdb5d729c99ce6e811e0c2f7b51705

Observation 1fe2b359-1e15-495e-8362-c2c476feda57 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Bracket formulation for irreversible classical fields.Physics Letters A, 100(8):423–427, 1984

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Observation 1a92301d-5a0a-4bef-b4ca-329a3720fd5c · outbound

This paper cites Dynamics and thermodynamics of complex fluids.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Dynamics and thermodynamics of complex fluids

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Observation 30b41ccc-5b15-4bfb-be3d-5a6f479535ae · outbound

This paper cites A review on data-driven constitutive laws for solids.Archives of Computational Methods in Engineering, pages 1–43, 2024.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling A review on data-driven constitutive laws for solids.Archives of Computational Methods in Engineering, pages 1–43, 2024

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Observation 01ee573f-e2d2-4691-8e69-0c9558b36bff · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Reflections on the motive power of fire, and on machines fitted to develop that power.Paris: Bachelier, 108(1824):1824, 1824

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Observation 2d369137-7d36-4124-8864-0e054dc8e093 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

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Observation 33949028-85b1-4843-9136-3fd798dcbe33 · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Ueber die bewegende kraft der wärme und die gesetze, welche sich daraus für die wärmelehre selbst ableiten lassen.Annalen der Physik, 155(3):368–397, 1850

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Observation f761090b-aa5c-44a2-9dcf-deeed340d13b · outbound

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work

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Observation 4a0be43c-e899-44ee-a887-853de0001433 · outbound

This paper cites Xv.—on the dynamical theory of heat, with numerical results deduced from mr joule’s equivalent of a thermal unit, and m.

A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Xv.—on the dynamical theory of heat, with numerical results deduced from mr joule’s equivalent of a thermal unit, and m

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source=pdf_text observed=2026-08-02T19:22:12.970502Z digest=sha256:951e3ba68765b449d3bbc7d3e0d625b780ae7159de25ad01776d6b22615a0a1c

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Quelques réflexions au sujet de la physique expérimentale

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Historical review of internal state variable theory for inelasticity

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling How the internal state of a plastically deformed body is to be described in a continuum theory

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Convex neural networks learn generalized standard material models.Journal of the Mechanics and Physics of Solids, 200:106103, 2025

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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Hammering at the entropy: A GENERIC-guided approach to learning polymeric rheological constitutive equations using PINNs

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