A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading | Paper Citation Record

Observation 1489cc45-0df1-4ea0-a08b-54320c0acd8d · outbound

This paper cites A new constitutive framework for arterial wall mechanics and a comparative study of material models,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A new constitutive framework for arterial wall mechanics and a comparative study of material models,

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This paper cites Hyperelastic constitutive modeling of hydrogels based on primary deformation modes and validation under 3D stress states,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Hyperelastic constitutive modeling of hydrogels based on primary deformation modes and validation under 3D stress states,

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This paper cites Visco-hyperelastic constitutive modeling of strain rate sensitive soft materials,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Visco-hyperelastic constitutive modeling of strain rate sensitive soft materials,

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This paper cites A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A physics-informed multi-agents model to predict thermo-oxidative/hydrolytic aging of elastomers,

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This paper cites On a fully three-dimensional finite-strain viscoelastic damage model: Formulation and computational aspects,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading On a fully three-dimensional finite-strain viscoelastic damage model: Formulation and computational aspects,

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This paper cites A THEORY OF FINITE VISCOELASTICITY AND NUMERICAL ASPECTS,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A THEORY OF FINITE VISCOELASTICITY AND NUMERICAL ASPECTS,

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This paper cites From machine learning to deep learning: progress in machine intelligence for rational drug discovery,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading From machine learning to deep learning: progress in machine intelligence for rational drug discovery,

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Observation 7039a654-507a-4d62-9b69-a91ee1364f85 · outbound

This paper cites Skamniotis, D.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Skamniotis, D

Reference 8

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This paper cites Parametric extended physics-informed neural networks for solid mechanics with complex mixed boundary conditions,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Parametric extended physics-informed neural networks for solid mechanics with complex mixed boundary conditions,

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This paper cites Physics-informed recovery of nonlinear residual stress fields in an inverse continuum framework,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Physics-informed recovery of nonlinear residual stress fields in an inverse continuum framework,

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This paper cites Enforcing physics onto PINNs for more accurate inhomogeneous material identification,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Enforcing physics onto PINNs for more accurate inhomogeneous material identification,

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This paper cites Statistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Statistical-Physics-Informed Neural Networks (Stat-PINNs): A Machine Learning Strategy for Coarse-graining Dissipative Dynamics,

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This paper cites Non -linear viscoelastic laws for soft biological tissues,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Non -linear viscoelastic laws for soft biological tissues,

Reference 13

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This paper cites Data-driven homogenisation of viscoelastic porous elastomers: Feedforward versus knowledge -based neural networks,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-driven homogenisation of viscoelastic porous elastomers: Feedforward versus knowledge -based neural networks,

Reference 14

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This paper cites Elasticity of soft tissues in simple elongation’,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Elasticity of soft tissues in simple elongation’,

Reference 15

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This paper cites On large strain viscoelasticity: Continuum formulation and finite element applications to elastomeric structures,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading On large strain viscoelasticity: Continuum formulation and finite element applications to elastomeric structures,

Reference 16

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This paper cites Nonlinear Solid Mechanics II,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Nonlinear Solid Mechanics II,

Reference 17

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This paper cites ON THE LARGE DEFORMATION BEHA VIOUR OF REINFORCED RUBBER AT DIFFERENT TEMPERATURES,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading ON THE LARGE DEFORMATION BEHA VIOUR OF REINFORCED RUBBER AT DIFFERENT TEMPERATURES,

Reference 18

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This paper cites Physics -driven neural networks for nonlinear micromechanics,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Physics -driven neural networks for nonlinear micromechanics,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Modeling finite-strain plasticity using physics - informed neural network and assessment of the network performance,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A physics-informed 3D surrogate model for elastic fields in polycrystals,

Reference 21

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This paper cites Data-driven elastoplastic constitutive modelling with physics -informed RNNs using the Virtual Fields Method for indirect training,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-driven elastoplastic constitutive modelling with physics -informed RNNs using the Virtual Fields Method for indirect training,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading The deep finite element method: A deep learning framework integrating the physics -informed neural networks with the finite element method,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Extended physics -informed extreme learning machine for linear elastic fracture mechanics,

Reference 24

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A physics-informed neural network-based method for dispersion calculations,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Viscohyperelastic Strain Energy Function,

Reference 26

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Inverse Physics-Informed Neural Networks for transport models in porous materials,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Viscoelastic constitutive law in large deformations: application to human knee ligaments and tendons,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Analytical and experimental study of shape memory alloy reinforcement on the performance of butt -fusion welded joints in high-density polyethylene pipe,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A Review of Recent Advances in Surrogate Models for Uncertainty Quantification of High-Dimensional Engineering Applications,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading History-Matching of Imbibition Flow in Multiscale Fractured Porous Media Using Physics-Informed Neural Networks (PINNs),

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Development and validation of subject-specific 3D human head models based on a nonlinear visco-hyperelastic constitutive framework,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A transversely isotropic viscohyperelastic material Application to the modeling of biological soft connective tissues,

Reference 35

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A Nonlinear Thermo -Visco- Green-Elastic Constitutive Model for Mullins Damage of Shape Memory Polymers under Giant Elongations,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Deep learning predicts path -dependent plasticity,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading THESE DE DOCTORAT DE L’ÉCOLE CENTRALE DE NANTES Auriane Platzer Rapporteurs avant soutenance : Composition du Jury

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading An analytical study on SMA beam-column actuators for anti -buckling phenomenon,

Reference 39

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This paper cites ‘A constitutive model for the Mullins effect with permanent set in a particle -reinforced rubber’ by A. Dorfmann and R.W. Ogden,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading ‘A constitutive model for the Mullins effect with permanent set in a particle -reinforced rubber’ by A. Dorfmann and R.W. Ogden,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Finite Bending of Fiber -Reinforced Visco-Hyperelastic Material: Analytical Approach and FEM,

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A viscoelastic constitutive model for compressible polymers based on logarithmic strain and its finite element implementation,

Reference 42

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading An analytical study on the bending of prismatic SMA beams,

Reference 43

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This paper cites Mechanics -informed, model -free symbolic regression framework for solving fracture problems,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Mechanics -informed, model -free symbolic regression framework for solving fracture problems,

Reference 44

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data -driven continuum damage mechanics with built -in physics,

Reference 45

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This paper cites A neural network -based enrichment of reproducing kernel approximation for modeling brittle fracture,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A neural network -based enrichment of reproducing kernel approximation for modeling brittle fracture,

Reference 46

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Tensor Basis Gaussian Process Models of Hyperelastic Materials

Reference 47

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A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Local approximate Gaussian process regression for data- driven constitutive models: development and comparison with neural networks,

Reference 48

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Observation 0b51c5a9-779a-46e2-b4c5-d47ef2c0df4e · outbound

This paper cites Data-driven hyperelasticity, Part I: A canonical isotropic formulation for rubberlike materials,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-driven hyperelasticity, Part I: A canonical isotropic formulation for rubberlike materials,

Reference 49

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Observation f3f2addb-b168-48ad-b796-2e1495dd80ad · outbound

This paper cites On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space -filling sampling,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space -filling sampling,

Reference 50

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:07.618847Z

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Observation 601088e4-76f4-4920-9e42-29db06781f5f · outbound

This paper cites Intriguing properties of neural networks.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Intriguing properties of neural networks

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:49.726524Z

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Observation 5934fb77-2ad7-48d4-86b8-a5b490eb71c6 · outbound

This paper cites Machine learning of evolving physics - based material models for multiscale solid mechanics,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Machine learning of evolving physics - based material models for multiscale solid mechanics,

Reference 52

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:07.384778Z

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Observation af83abc4-fd14-4b0e-9997-415a907a74a7 · outbound

This paper cites Prediction of damage evolution in CMCs considering the real microstructures through a deep-learning scheme,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Prediction of damage evolution in CMCs considering the real microstructures through a deep-learning scheme,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:49.892749Z

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Observation 1bf0aa30-a9df-43f0-8f30-7ebbcba81744 · outbound

This paper cites t -PiNet: A thermodynamics -informed hierarchical learning for discovering constitutive relations of geomaterials,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading t -PiNet: A thermodynamics -informed hierarchical learning for discovering constitutive relations of geomaterials,

Reference 54

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:07.153879Z

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Observation 81499f42-6a4b-4ecd-a893-53c4725d411f · outbound

This paper cites A new family of Constitutive Artificial Neural Networks towards automated model discovery,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A new family of Constitutive Artificial Neural Networks towards automated model discovery,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.122115Z

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Observation 6a71b6ae-428b-4fad-80bd-86039996856c · outbound

This paper cites Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Physics-informed data-driven discovery of constitutive models with application to strain-rate-sensitive soft materials,

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.207784Z

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Observation 0170f964-f285-4468-8e8f-bbeb86170bfe · outbound

This paper cites Thermodynamics-based Artificial Neural Networks for constitutive modeling,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Thermodynamics-based Artificial Neural Networks for constitutive modeling,

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.297235Z

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Observation 669b4fd4-c73b-40cc-a7cf-68721c636da2 · outbound

This paper cites Evolution TANN and the identification of internal variables and evolution equations in solid mechanics,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Evolution TANN and the identification of internal variables and evolution equations in solid mechanics,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.392125Z

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Observation c2d5a15b-d3b8-46af-8554-741077b97de3 · outbound

This paper cites A review on data-driven constitutive laws for solids.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A review on data-driven constitutive laws for solids

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.580526Z

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Observation 1987910d-7df8-4f88-a1ad-671d485de377 · outbound

This paper cites Parametrized polyconvex hyperelasticity with physics-augmented neural networks,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Parametrized polyconvex hyperelasticity with physics-augmented neural networks,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.660894Z

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Observation e25a7a77-d8d9-4d90-bc4b-4a80c2c0929d · outbound

This paper cites Finite electro-elasticity with physics- augmented neural networks,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Finite electro-elasticity with physics- augmented neural networks,

Reference 62

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:06.395868Z

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Observation 5841e5f3-fa96-4be6-afad-7567cd57b762 · outbound

This paper cites Input Convex Neural Networks,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Input Convex Neural Networks,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:50.871306Z

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Observation 5756fac2-275e-4d93-a3f9-cc3e502df048 · outbound

This paper cites A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A mechanics-informed deep learning framework for data-driven nonlinear viscoelasticity,

Reference 64

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:06.161265Z

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Observation 633b6270-48bc-49ec-b02c-4ba2a8ef80c2 · outbound

This paper cites A mechanics-informed artificial neural network approach in data- driven constitutive modeling,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A mechanics-informed artificial neural network approach in data- driven constitutive modeling,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:51.076479Z

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Observation 23c0e07f-7222-4b6c-8bd8-bbaf52b7a9e2 · outbound

This paper cites Hypersparse Neural Network Analysis of Large-Scale Internet Traffic,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Hypersparse Neural Network Analysis of Large-Scale Internet Traffic,

Reference 66

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:05.856638Z

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Observation da7d3c2e-9c85-4646-a5c3-b6717d2e575c · outbound

This paper cites Data-Driven Network Neuroscience: On Data Collection and Benchmark.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-Driven Network Neuroscience: On Data Collection and Benchmark

Reference 67

Resolution: verified exact; doi, observed 2026-08-06T16:47:57.428771Z

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Observation 16e7da01-2609-4953-8b85-59822e53b517 · outbound

This paper cites Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Patient Risk Assessment and Warning Symptom Detection Using Deep Attention-Based Neural Networks

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:48:05.587834Z

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Observation 593a3d59-1c1b-4148-9b82-ec6a8779323f · outbound

This paper cites Predicting Progression Events in Multiple Myeloma from Routine Blood Work.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Predicting Progression Events in Multiple Myeloma from Routine Blood Work

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:48:05.403449Z

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Observation 4144a0ff-cc4e-4426-81ca-a51c1a90644c · outbound

This paper cites A machine learning approach to predict in vivo skin growth,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A machine learning approach to predict in vivo skin growth,

Reference 70

Resolution: verified exact; doi, observed 2026-08-06T16:47:57.203126Z

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Observation 93e4da4b-2afa-4980-912f-031f240319fa · outbound

This paper cites Discovering plasticity models without stress data,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Discovering plasticity models without stress data,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:51.749490Z

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Observation e3cde35c-ecf8-485d-b172-5cd3318eb982 · outbound

This paper cites Bayesian -EUCLID: Discovering hyperelastic material laws with uncertainties,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Bayesian -EUCLID: Discovering hyperelastic material laws with uncertainties,

Reference 72

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:05.256840Z

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Observation 996415f9-802d-405e-a6e2-7e052e96dd40 · outbound

This paper cites Automated identification of linear viscoelastic constitutive laws with EUCLID,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Automated identification of linear viscoelastic constitutive laws with EUCLID,

Reference 73

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:04.983526Z

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Observation 805a0b93-e397-4e6e-a4e0-5b3c0a2fccfe · outbound

This paper cites Learning viscoelasticity models from indirect data using deep neural networks,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Learning viscoelasticity models from indirect data using deep neural networks,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:52.033084Z

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Observation bd3011ed-c1c5-4715-be05-3b9aa9707d59 · outbound

This paper cites Hybrid Monte Carlo for Failure Probability Estimation with Gaussian Process Surrogates,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Hybrid Monte Carlo for Failure Probability Estimation with Gaussian Process Surrogates,

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:52.246989Z

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Observation d4fb1e4d-241d-4858-bb1c-242263f52e75 · outbound

This paper cites Viscoelastic constitutive artificial neural networks (vCANNs) – A framework for data -driven anisotropic nonlinear finite viscoelasticity,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Viscoelastic constitutive artificial neural networks (vCANNs) – A framework for data -driven anisotropic nonlinear finite viscoelasticity,

Reference 77

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:04.463354Z

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Observation 8473e3f3-5407-4c10-9cf0-0f2e6a2048fa · outbound

This paper cites Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Constitutive artificial neural networks: A fast and general approach to predictive data-driven constitutive modeling by deep learning,

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:52.431887Z

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Observation 06a59bfb-8a6d-4c33-9ee2-b1d01b4ff3cd · outbound

This paper cites Discovering uncertainty: Bayesian constitutive artificial neural networks,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Discovering uncertainty: Bayesian constitutive artificial neural networks,

Reference 79

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:06.620556Z

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Observation 75271d4c-f739-4eea-8bae-600f810ac769 · outbound

This paper cites ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading ViscoelasticNet: A physics informed neural network framework for stress discovery and model selection,

Reference 81

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:04.705595Z

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Observation cdd14227-9cc6-4d10-bd85-9a9d146a356b · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:52.802361Z

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Observation 3125cb98-60e3-4471-abc1-9a72f74c26e0 · outbound

This paper cites Model -free Data-Driven viscoelasticity in the frequency domain,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Model -free Data-Driven viscoelasticity in the frequency domain,

Reference 83

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:03.883873Z

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Observation b52bbf19-e6d0-4feb-ad62-11fada4a3e58 · outbound

This paper cites Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:52.922749Z

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Observation 208d2215-5618-4b73-a711-6939cb488135 · outbound

This paper cites Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:52.998867Z

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Observation 67817210-038b-4e05-84dc-7297978a6248 · outbound

This paper cites Weight Uncertainty in Neural Networks.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Weight Uncertainty in Neural Networks

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:53.055634Z

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Observation 4df1c991-4818-4d65-a111-79904920721c · outbound

This paper cites What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:53.183848Z

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Observation 17d047f5-3c1e-41e9-86a3-61f9953f6aaf · outbound

This paper cites A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A generalized dual potential for inelastic Constitutive Artificial Neural Networks: A JAX implementation at finite strains,

Reference 88

Resolution: verified exact; raw_fallback, observed 2026-08-06T16:48:04.101075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d199980c-9658-4579-84c3-5cedb00cac49 · outbound

This paper cites Bayesian Physics Informed Neural Networks for real -world nonlinear dynamical systems,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Bayesian Physics Informed Neural Networks for real -world nonlinear dynamical systems,

Reference 89

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:03.609878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3a0bfde3-5178-4291-a09d-feaf2321c11b · outbound

This paper cites Empowering approximate Bayesian neural networks with functional priors through anchored ensembling for mechanics surrogate modeling applications,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Empowering approximate Bayesian neural networks with functional priors through anchored ensembling for mechanics surrogate modeling applications,

Reference 90

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:03.371486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fd3e0346-962a-4a7e-9c3c-3599fe540a27 · outbound

This paper cites Uncertainty quantification for noisy inputs -outputs in physics-informed neural networks and neural operators,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Uncertainty quantification for noisy inputs -outputs in physics-informed neural networks and neural operators,

Reference 91

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:03.121642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b42b0096-5b7c-4d07-81e9-60b81281dbe1 · outbound

This paper cites FE ANN : an efficient data -driven multiscale approach based on physics-constrained neural networks and automated data mining,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading FE ANN : an efficient data -driven multiscale approach based on physics-constrained neural networks and automated data mining,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:53.599219Z

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Observation 16059ef9-10f0-4335-b028-7c24958444f3 · outbound

This paper cites Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Viscoelasticty with physics-augmented neural networks: Model formulation and training methods without prescribed internal variables

Reference 93

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:48:02.933431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5a86fe6b-0c7a-4349-a72e-00f278063435 · outbound

This paper cites Neural network - based multiscale modeling of finite strain magneto -elasticity with relaxed convexity criteria,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Neural network - based multiscale modeling of finite strain magneto -elasticity with relaxed convexity criteria,

Reference 94

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:48:02.780598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 80f98a3d-2ab9-483b-8a21-473b1c0de2d7 · outbound

This paper cites Data-oriented constitutive modeling of plasticity in metals,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-oriented constitutive modeling of plasticity in metals,

Reference 95

Resolution: verified exact; doi, observed 2026-08-06T16:47:56.889482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 492e153d-f8e7-49a0-b7c2-3dd3cd238422 · outbound

This paper cites Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Application of artificial neural networks for the prediction of interface mechanics: a study on grain boundary constitutive behavior,

Reference 96

Resolution: verified exact; doi, observed 2026-08-06T16:47:56.641942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 28abf71f-0cda-4ae1-b650-66b56fef7b6c · outbound

This paper cites Multiscale Modeling Meets Machine Learning: What Can We Learn?,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Multiscale Modeling Meets Machine Learning: What Can We Learn?,

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-06T16:47:54.042695Z

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Observation a7c93778-c5eb-4b0b-8bd3-ba3e5a3125ef · outbound

This paper cites Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences,

Reference 98

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This paper cites Hernández, A.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Hernández, A

Reference 99

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Observation 6957d47e-373b-4be1-a5d9-45df9ff7ffef · outbound

This paper cites Data-driven computational mechanics,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Data-driven computational mechanics,

Reference 100

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This paper cites Model-Free Data-Driven inelasticity,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Model-Free Data-Driven inelasticity,

Reference 101

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This paper cites On sparse regression, Lp -regularization, and automated model discovery,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading On sparse regression, Lp -regularization, and automated model discovery,

Reference 102

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This paper cites Keeton, Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Keeton, Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation

Reference 103

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Observation 9faf612e-049d-4e0b-b785-d5c6d0889850 · outbound

This paper cites A POD-TANN approach for the multiscale modeling of materials and macroelement derivation in geomechanics.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading A POD-TANN approach for the multiscale modeling of materials and macroelement derivation in geomechanics

Reference 104

Resolution: verified exact; local_arxiv, observed 2026-08-06T16:48:02.322651Z

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Observation eb5abedf-c5ff-457a-bce0-6b34ec0946fa · outbound

This paper cites Tunnel lining defects identification using TPE-CatBoost algorithm with GPR data: A model test study,.

A Physics-Informed Data-Driven Discovery for Constitutive Modeling of Compressible, Nonlinear, History-Dependent Soft Materials under Multiaxial Cyclic Loading Tunnel lining defects identification using TPE-CatBoost algorithm with GPR data: A model test study,

Reference 105

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