Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics | Paper Citation Record

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This paper cites Deep Variational Information Bottleneck.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Deep Variational Information Bottleneck

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Input convex neural networks

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This paper cites Statistical Mechanics for Chemistry and Materials Science.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Statistical Mechanics for Chemistry and Materials Science

Reference 3

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Reaction coordinates and rates from transition paths

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This paper cites Phase-field models for microstructure evolution.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Phase-field models for microstructure evolution

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This paper cites Diffusion maps.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Diffusion maps

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This paper cites Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Diffusion maps, reduction coordinates, and low dimensional representation of stochastic systems

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This paper cites Thermodynamics with internal state variables.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Thermodynamics with internal state variables

Reference 8

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This paper cites An approximation theorem for functionals, with applications in continuum mechanics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics An approximation theorem for functionals, with applications in continuum mechanics

Reference 9

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This paper cites Foundations of linear viscoelasticity.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Foundations of linear viscoelasticity

Reference 10

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Elements of information theory

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This paper cites Identifying structural flow defects in disordered solids using machine-learning methods.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Identifying structural flow defects in disordered solids using machine-learning methods

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This paper cites Onsager’s variational principle in soft matter.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Onsager’s variational principle in soft matter

Reference 13

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This paper cites Computing committors in collective variables via Mahalanobis diffusion maps.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Computing committors in collective variables via Mahalanobis diffusion maps

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This paper cites Multiscale mass-spring models of carbon nanotube foams.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Multiscale mass-spring models of carbon nanotube foams

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This paper cites Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Machine learning force fields and coarse-grained variables in molecular dynamics: application to materials and biological systems

Reference 16

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This paper cites Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems

Reference 17

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This paper cites Deep learning.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Deep learning

Reference 18

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This paper cites A presentation and comparison of two large deformation viscoelasticity models.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics A presentation and comparison of two large deformation viscoelasticity models

Reference 19

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This paper cites Variational encoding of complex dynamics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Variational encoding of complex dynamics

Reference 20

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

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics On large strain viscoelasticity: continuum formulation and finite element applications to elastomeric structures

Reference 21

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This paper cites Neural autoregressive flows.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Neural autoregressive flows

Reference 22

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This paper cites Variational Onsager Neural Networks (VONNs): A thermodynamics-based variational learning strategy for non-equilibrium PDEs.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Variational Onsager Neural Networks (VONNs): A thermodynamics-based variational learning strategy for non-equilibrium PDEs

Reference 23

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Principal components in regression analysis

Reference 24

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This paper cites Principal component analysis: a review and recent developments.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Principal component analysis: a review and recent developments

Reference 25

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This paper cites Nonlinear elasto-plastic model for dense granular flow.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Nonlinear elasto-plastic model for dense granular flow

Reference 26

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This paper cites A statistical mechanics derivation and implementation of non-conservative phase field models for front propagation in elastic media.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics A statistical mechanics derivation and implementation of non-conservative phase field models for front propagation in elastic media

Reference 27

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics A statistical mechanics framework for constructing nonequilibrium thermodynamic models

Reference 28

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Finite Strain Elastic-Plastic Theory with Application to Plane Wave Analysis

Reference 29

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This paper cites Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders

Reference 30

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This paper cites Thermomechanical theory of martensitic phase transformations in inelastic materials.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Thermomechanical theory of martensitic phase transformations in inelastic materials

Reference 31

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This paper cites Learning macroscopic internal variables and history dependence from microscopic models.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Learning macroscopic internal variables and history dependence from microscopic models

Reference 32

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This paper cites On the thermodynamic foundations of non-linear solid mechanics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics On the thermodynamic foundations of non-linear solid mechanics

Reference 33

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Plasticity theory

Reference 34

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Evolution TANN and the discovery of the internal variables and evolution equations in solid mechanics

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Multiscale modeling of inelastic materials with Thermodynamics-based Artificial Neural Networks (TANN)

Reference 36

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics The thermomechanics of nonlinear irreversible behaviours

Reference 37

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This paper cites Thermodynamics with Internal Variables. Part I. General Concepts.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Thermodynamics with Internal Variables. Part I. General Concepts

Reference 38

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This paper cites Thermodynamics with Internal Variables. Part II. Applications.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Thermodynamics with Internal Variables. Part II. Applications

Reference 39

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This paper cites Continuum damage theory—application to concrete.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Continuum damage theory—application to concrete

Reference 40

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This paper cites Self-adaptive physics-informed neural networks.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Self-adaptive physics-informed neural networks

Reference 41

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Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Machine learning: a probabilistic perspective

Reference 42

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This paper cites Internal variables in non-equilibrium thermodynamics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Internal variables in non-equilibrium thermodynamics

Reference 43

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This paper cites Beyond equilibrium thermodynamics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Beyond equilibrium thermodynamics

Reference 44

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This paper cites Statistical Mechanics: International Series of Monographs in Natural Philosophy.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Statistical Mechanics: International Series of Monographs in Natural Philosophy

Reference 45

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This paper cites Reaction coordinates and mechanistic hypothesis tests.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Reaction coordinates and mechanistic hypothesis tests

Reference 46

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This paper cites Thermodynamics of rate-independent plasticity.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Thermodynamics of rate-independent plasticity

Reference 47

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This paper cites Incompressible inelasticity as an essential ingredient for the validity of the kinematic decomposition F= FeFi.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Incompressible inelasticity as an essential ingredient for the validity of the kinematic decomposition F= FeFi

Reference 48

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This paper cites Derivation of F= FeFp as the continuum limit of crystalline slip.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Derivation of F= FeFp as the continuum limit of crystalline slip

Reference 49

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Observation 82f0f367-1a92-4edc-a1d7-d3ed4189c481 · outbound

This paper cites Kinematics of elasto-plasticity: Validity and limits of applicability of F= FeFp for general three-dimensional deformations.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Kinematics of elasto-plasticity: Validity and limits of applicability of F= FeFp for general three-dimensional deformations

Reference 50

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Observation 5dd0209c-7d58-4023-b1bf-848632814c0a · outbound

This paper cites Inelastic constitutive relations for solids: an internal-variable theory and its application to metal plasticity.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Inelastic constitutive relations for solids: an internal-variable theory and its application to metal plasticity

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:06.123398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1639098b-9d52-48af-9f7f-3575f8a1fcf1 · outbound

This paper cites Inelastic constitutive relations for solids: an internal-variable theory and its application to metal plasticity.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Inelastic constitutive relations for solids: an internal-variable theory and its application to metal plasticity

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:06.100802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0988ccee-5248-45df-8dfe-164ec7843224 · outbound

This paper cites Stress-dependent finite growth in soft elastic tissues.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Stress-dependent finite growth in soft elastic tissues

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:06.072401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fa36c4a5-032c-4ff7-b19b-d82716d87608 · outbound

This paper cites Determination of reaction coordinates via locally scaled diffusion map.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Determination of reaction coordinates via locally scaled diffusion map

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:06.037931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a08fb9ac-3981-45df-94c4-6b98754b8d49 · outbound

This paper cites Scaffolds, levers, rods and springs: diverse cellular functions of long coiled-coil proteins.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Scaffolds, levers, rods and springs: diverse cellular functions of long coiled-coil proteins

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:06.004142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 582b1334-5b11-4023-b03e-0c2aa9e3b506 · outbound

This paper cites Hyper-reduction of mechanical models involving internal variables.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Hyper-reduction of mechanical models involving internal variables

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.974120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation bd2ef92c-75da-4d00-8e73-f78c69116996 · outbound

This paper cites Second law, entropy production, and reversibility in thermodynamics of information.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Second law, entropy production, and reversibility in thermodynamics of information

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.950251Z

Source-reported events for the cited work

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Observation 946240e4-a3e9-4b62-b1b6-d055c4c6726e · outbound

This paper cites Relationship between local structure and relaxation in out-of-equilibrium glassy systems.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Relationship between local structure and relaxation in out-of-equilibrium glassy systems

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.927447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e23778b0-c9d9-4135-87c2-a0f15de753fb · outbound

This paper cites Statistical mechanics: entropy, order parameters, and complexity.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Statistical mechanics: entropy, order parameters, and complexity

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.903846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 843df122-2dbc-4f1a-8022-66e9df5dc84c · outbound

This paper cites Elasto-viscoplastic phase field modelling of anisotropic cleavage fracture.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Elasto-viscoplastic phase field modelling of anisotropic cleavage fracture

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.870722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 22bec120-6c53-4bbc-b0ba-ac564647dc66 · outbound

This paper cites Information bottleneck approach to predictive inference.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Information bottleneck approach to predictive inference

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.841956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 1a3b9385-4f4c-4cc7-8c8d-93a742b1b382 · outbound

This paper cites Thermodynamics of prediction.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Thermodynamics of prediction

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.815700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 23957019-58c9-4f8f-90d3-b5afcbb0ca22 · outbound

This paper cites Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Machine learning materials physics: Integrable deep neural networks enable scale bridging by learning free energy functions

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.786773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 49df8c8f-c361-4682-8989-75677e18e9dc · outbound

This paper cites Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Scale bridging materials physics: Active learning workflows and integrable deep neural networks for free energy function representations in alloys

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.764160Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 02da389c-cfe3-4489-8833-85264e8dc5d9 · outbound

This paper cites A thermomechanical constitutive model for cemented granular materials with quantifiable internal variables. Part I—Theory.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics A thermomechanical constitutive model for cemented granular materials with quantifiable internal variables. Part I—Theory

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.738531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7cf85d92-03d4-4fe0-b7d7-3f8b07a2d8a3 · outbound

This paper cites The information bottleneck method.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics The information bottleneck method

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-10T04:32:05.201853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15e8af3b-a747-4a10-a043-b72be1cfc5de · outbound

This paper cites Combined molecular/continuum modeling reveals the role of friction during fast unfolding of coiled-coil proteins.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Combined molecular/continuum modeling reveals the role of friction during fast unfolding of coiled-coil proteins

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.710872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 54485a81-0d7f-4fbd-80a1-262a16e413f3 · outbound

This paper cites Roadmap on multiscale materials modeling.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Roadmap on multiscale materials modeling

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.683368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation cd531680-ac4c-431f-b24a-a244202ecfc0 · outbound

This paper cites Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Interpretable embeddings from molecular simulations using Gaussian mixture variational autoencoders

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.658770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97f98a7f-795b-4193-bfd1-1509fc30179c · outbound

This paper cites Universal approximation of functions on sets.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Universal approximation of functions on sets

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.629055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6f794b75-ef00-4c97-8159-8d4d478bb4ef · outbound

This paper cites When and why PINNs fail to train: A neural tangent kernel perspective.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics When and why PINNs fail to train: A neural tangent kernel perspective

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.601015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9853c82-199f-4ecd-a73c-ea1c5070b166 · outbound

This paper cites Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics

Reference 72

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T04:32:05.565774Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c145c9df-b303-46dc-a3ae-ba24f70a3811 · outbound

This paper cites Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.538636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation b6790d98-51b1-4f6a-9dbf-c02864214dbf · outbound

This paper cites Learning Likelihoods with Conditional Normalizing Flows.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Learning Likelihoods with Conditional Normalizing Flows

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T04:32:05.281151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9736105f-7a35-4a6a-b868-7055f4f37007 · outbound

This paper cites Deep sets.

Bridging statistical mechanics and thermodynamics away from equilibrium: a data-driven approach for learning internal variables and their dynamics Deep sets

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:32:05.508214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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