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Paper Citation Record · LEDGER
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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Source: paper_references, paper_reference_links, observed 2026-08-01T10:11:58.786716Z
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100 of 115 outbound references displayed
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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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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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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work
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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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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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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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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work
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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work
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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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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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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Efficiently parameterized neural metriplectic systems
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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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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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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work
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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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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Unresolved cited work
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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Graphical methods in the thermodynamics of fluids.Transactions of the Connecticut Academy of Arts and Sciences, 1:309–342, 1873
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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Reciprocal relations in irreversible processes
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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Reciprocal relations in irreversible processes
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A hierarchy of thermodynamics learning frameworks for inelastic constitutive modeling Springer, 2008
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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 The thermal conductivity of liquids under pressure
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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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