Structure-preserving uncertainty quantification for GENERIC dynamics | Paper Citation Record

Observation b63c67ad-89ed-4781-be77-e12b1fe0417d · outbound

This paper cites Neural ordinary differential equa- tions.Advances in neural information processing systems, 31, 2018.

Structure-preserving uncertainty quantification for GENERIC dynamics Neural ordinary differential equa- tions.Advances in neural information processing systems, 31, 2018

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Structure-preserving uncertainty quantification for GENERIC dynamics Unresolved cited work

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Observation 6e0b4d7e-7378-41ab-b10f-729c7dc99910 · outbound

This paper cites Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021.

Structure-preserving uncertainty quantification for GENERIC dynamics Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440, 2021

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This paper cites Learning nonlinear oper- ators via DeepONet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021.

Structure-preserving uncertainty quantification for GENERIC dynamics Learning nonlinear oper- ators via DeepONet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021

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Observation e61e4fe4-35bf-488a-a6f5-251e23dda891 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Fourier Neural Operator for Parametric Partial Differential Equations

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Observation a1087b49-db4b-430c-9610-482109e6f830 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Neural operator: Learning maps between function spaces with applications to pdes

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Observation 94264ff0-e398-4bf6-9d4e-1ecd08a66dd3 · outbound

This paper cites Structure-preserving deep learning.European journal of applied mathematics, 32(5):888– 936, 2021.

Structure-preserving uncertainty quantification for GENERIC dynamics Structure-preserving deep learning.European journal of applied mathematics, 32(5):888– 936, 2021

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Observation e6b0ff6c-3494-4e09-bbeb-2c74e86961f3 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics SympNets: Intrinsic structure- preserving symplectic networks for identifying Hamiltonian systems.Neural Networks, 132:166–179, 2020

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Observation 2d6fff90-4628-4712-b4de-e872f50186af · outbound

This paper cites OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle.Physical Review Fluids, 6(11):114402, 2021.

Structure-preserving uncertainty quantification for GENERIC dynamics OnsagerNet: Learning stable and interpretable dynamics using a generalized Onsager principle.Physical Review Fluids, 6(11):114402, 2021

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Observation 010e8e61-ba33-40ec-b393-a5f0f2e320e8 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Unresolved cited work

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Observation 7f274bd6-9fcd-4a1c-8988-1cae11aa2f12 · outbound

This paper cites Hamiltonian Neural Networks.Advances in neural information processing systems, 32, 2019.

Structure-preserving uncertainty quantification for GENERIC dynamics Hamiltonian Neural Networks.Advances in neural information processing systems, 32, 2019

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Observation 1dae53f6-d6d6-46f3-ba1b-dc2755e8bcaa · outbound

This paper cites Lagrangian Neural Networks.

Structure-preserving uncertainty quantification for GENERIC dynamics Lagrangian Neural Networks

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Observation 8dcde9fe-9d9e-4101-89d7-88c2c35c420d · outbound

This paper cites Symplectic ODE-Net: Learning Hamiltonian dynamics with control.

Structure-preserving uncertainty quantification for GENERIC dynamics Symplectic ODE-Net: Learning Hamiltonian dynamics with control

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Observation 57b3a93c-7e62-4ae3-856e-642b395dd680 · outbound

This paper cites Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks.IEEE Transactions on Neural Networks and Learning Systems, 34(11):8271–8283, 2022.

Structure-preserving uncertainty quantification for GENERIC dynamics Learning Poisson systems and trajectories of autonomous systems via Poisson neural networks.IEEE Transactions on Neural Networks and Learning Systems, 34(11):8271–8283, 2022

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Observation 881904f8-6f79-49d8-8d72-e810df9ae7dd · outbound

This paper cites Dynamics and thermodynamics of complex fluids.

Structure-preserving uncertainty quantification for GENERIC dynamics Dynamics and thermodynamics of complex fluids

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Observation f34bd3d0-6e9f-4b2b-ae0f-433197dc14a7 · outbound

This paper cites Dynamics and thermodynamics of complex fluids.

Structure-preserving uncertainty quantification for GENERIC dynamics Dynamics and thermodynamics of complex fluids

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Observation 8c5559ab-53fb-4390-aae7-25671327d0ae · outbound

This paper cites John Wiley & Sons, 2005.

Structure-preserving uncertainty quantification for GENERIC dynamics John Wiley & Sons, 2005

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Observation 97e0d1f8-6213-4967-aff6-b80f1e83e356 · outbound

This paper cites Walter de Gruyter GmbH & Co KG, 2018.

Structure-preserving uncertainty quantification for GENERIC dynamics Walter de Gruyter GmbH & Co KG, 2018

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Observation ac52ef7f-0e06-4848-8c9b-a3f2ffa84156 · outbound

This paper cites A paradigm for joined Hamiltonian and dissipative systems.Physica D: Nonlinear Phenom- ena, 18(1-3):410–419, 1986.

Structure-preserving uncertainty quantification for GENERIC dynamics A paradigm for joined Hamiltonian and dissipative systems.Physica D: Nonlinear Phenom- ena, 18(1-3):410–419, 1986

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Observation b329c58d-daf9-4a03-b0b2-5aab61f069b8 · outbound

This paper cites Structure-preserving neural networks.Journal of Computational Physics, 426:109950, 2021.

Structure-preserving uncertainty quantification for GENERIC dynamics Structure-preserving neural networks.Journal of Computational Physics, 426:109950, 2021

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Structure-preserving uncertainty quantification for GENERIC dynamics Machine learning structure preserving brackets for forecasting irreversible processes.Advances in Neural Information Processing Systems, 34:5696–5707, 2021

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This paper cites Efficiently parameterized neural metriplectic systems.

Structure-preserving uncertainty quantification for GENERIC dynamics Efficiently parameterized neural metriplectic systems

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Observation 2a6dfe0d-27ba-4fb7-8ff7-2a3822736273 · outbound

This paper cites Nonlinear GENERIC-Embedded Neural Networks (N-GENNs): Learning GENERIC dynamics with non-quadratic dissipation potentials.

Structure-preserving uncertainty quantification for GENERIC dynamics Nonlinear GENERIC-Embedded Neural Networks (N-GENNs): Learning GENERIC dynamics with non-quadratic dissipation potentials

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Observation 1bddc16d-2b37-48c7-8c23-af1b2649ed97 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Unresolved cited work

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Structure-preserving uncertainty quantification for GENERIC dynamics Uncertainty quantifi- cation in scientific machine learning: Methods, metrics, and comparisons.Journal of Computational Physics, 477:111902, 2023

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Observation 6aadbe95-1761-4a07-a79e-5fbcfc96f4f7 · outbound

This paper cites Springer Science & Business Media, 2012.

Structure-preserving uncertainty quantification for GENERIC dynamics Springer Science & Business Media, 2012

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Structure-preserving uncertainty quantification for GENERIC dynamics Bayesian Neural Networks: An introduction and survey

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Structure-preserving uncertainty quantification for GENERIC dynamics Simple and scalable predictive uncertainty estimation using deep ensembles.Advances in neural information processing systems, 30, 2017

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Structure-preserving uncertainty quantification for GENERIC dynamics Dropout as a Bayesian approximation: Representing model uncertainty in deep learning

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Structure-preserving uncertainty quantification for GENERIC dynamics B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data.Journal of Computational Physics, 425:109913, 2021

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Structure-preserving uncertainty quantification for GENERIC dynamics Adversarial uncertainty quantification in physics-informed neural networks

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Structure-preserving uncertainty quantification for GENERIC dynamics Wasserstein generative adversarial uncertainty quantification in physics- informed neural networks.Journal of Computational Physics, 463:111270, 2022

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Structure-preserving uncertainty quantification for GENERIC dynamics PI-V AE: Physics-Informed Variational Auto-encoder for stochastic differ- ential equations.Computer Methods in Applied Mechanics and Engineering, 403:115664, 2023

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Structure-preserving uncertainty quantification for GENERIC dynamics Physics-informed variational inference for uncertainty quantification of stochastic differential equations.Journal of Computational Physics, 487:112183, 2023

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Structure-preserving uncertainty quantification for GENERIC dynamics Physics-informed polynomial chaos expansions

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Structure-preserving uncertainty quantification for GENERIC dynamics A conformal prediction framework for uncertainty quantification in physics-informed neural networks.Journal of Computational Physics, 561:114979, 2026

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Observation 8828b9c0-56a4-41a9-8821-2b98deb8a5c1 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Unresolved cited work

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Structure-preserving uncertainty quantification for GENERIC dynamics Learning thermodynamically constrained equations of state with uncertainty.APL Machine Learning, 2(1), 2024

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Structure-preserving uncertainty quantification for GENERIC dynamics Bayesian-EUCLID: Discovering hyperelastic material laws with uncertainties.Computer Methods in Applied Mechanics and Engineering, 398:115225, 2022

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Structure-preserving uncertainty quantification for GENERIC dynamics Conformal quantile regression for neural probabilistic constitutive modeling.Computer Methods in Applied Mechanics and Engineering, 457:118981, 2026

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Observation f62163a9-97da-400c-a3cc-ca310f1891a9 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Hands- on Bayesian neural networks—a tutorial for deep learning users.IEEE Computational Intelligence Magazine, 17(2):29–48, 2022

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Structure-preserving uncertainty quantification for GENERIC dynamics A survey of uncertainty in deep neural networks.Artificial intelligence review, 56(Suppl 1):1513–1589, 2023

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Observation 026833cc-81e0-4217-9a7d-80322a94eba3 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Epistemic neural networks.Advances in Neural Information Processing Systems, 36:2795–2823, 2023

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fe8f051b-faca-46ee-a47e-7a047cc9821b · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics Composite Bayesian optimization in function spaces using NEON—Neural Epistemic Operator Networks.Scientific Reports, 14(1):29199, 2024

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Source-reported events for the cited work

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Structure-preserving uncertainty quantification for GENERIC dynamics E-PINNs: Epistemic Physics- Informed Neural Networks.arXiv preprint arXiv:2503.19333, 2025

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Observation ef1be9ea-64e8-4843-924a-81882535756b · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics EVODMs: variational learning of PDEs for stochastic systems via diffusion models with quantified epistemic uncertainty.Journal of Computational Physics, page 114722, 2026

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Observation 4f41f8a6-083c-4cdc-b7b6-ffe8f053daa8 · outbound

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Structure-preserving uncertainty quantification for GENERIC dynamics SPIEDiff: robust learning of long-time macroscopic dynamics from short-time particle simulations with quantified epistemic uncertainty

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Observation a4134103-f613-4c4a-a501-e93d79d6b6a9 · outbound

This paper cites Conformal prediction: A gentle introduction.Foundations and Trends in Machine Learning, 16(4):494–591, 2023.

Structure-preserving uncertainty quantification for GENERIC dynamics Conformal prediction: A gentle introduction.Foundations and Trends in Machine Learning, 16(4):494–591, 2023

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:20.797976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12ab3adf-490d-4831-864b-7e227b04c65f · outbound

This paper cites GENERIC guide to the multiscale dynamics and thermodynamics.Journal of Physics Com- munications, 2(3):032001, 2018.

Structure-preserving uncertainty quantification for GENERIC dynamics GENERIC guide to the multiscale dynamics and thermodynamics.Journal of Physics Com- munications, 2(3):032001, 2018

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:20.802950Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e70d7f9-5132-49eb-91f9-48360e70cec5 · outbound

This paper cites Fluctuation symmetry leads to GENERIC equations with non-quadratic dissipation.Stochastic Processes and their Applications, 130(1):139– 170, 2020.

Structure-preserving uncertainty quantification for GENERIC dynamics Fluctuation symmetry leads to GENERIC equations with non-quadratic dissipation.Stochastic Processes and their Applications, 130(1):139– 170, 2020

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:20.808134Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79db2c49-fdbc-4e51-99fa-95db9358c6c9 · outbound

This paper cites Input convex neural networks.

Structure-preserving uncertainty quantification for GENERIC dynamics Input convex neural networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:20.812177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d3e06d3-e0ac-4d9c-a23b-43ace090b9c3 · outbound

This paper cites NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators.SIAM Review, 66(1):161–190, 2024.

Structure-preserving uncertainty quantification for GENERIC dynamics NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators.SIAM Review, 66(1):161–190, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:23.578238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation db1cb78d-79d8-4dfa-8ef3-a1a4278e0a48 · outbound

This paper cites Evaluating and calibrating uncertainty prediction in regression tasks.Sensors, 22(15):5540, 2022.

Structure-preserving uncertainty quantification for GENERIC dynamics Evaluating and calibrating uncertainty prediction in regression tasks.Sensors, 22(15):5540, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:20.822704Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 638c93f0-5d0c-4508-a24f-b7e8f3167710 · outbound

This paper cites Accurate uncertainties for deep learning using cali- brated regression.

Structure-preserving uncertainty quantification for GENERIC dynamics Accurate uncertainties for deep learning using cali- brated regression

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:23.413415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2bb5ac12-9a0a-478f-8ac8-ddeac1ebdd47 · outbound

This paper cites CRUDE: Calibrating Regression Uncertainty Distributions Empirically.

Structure-preserving uncertainty quantification for GENERIC dynamics CRUDE: Calibrating Regression Uncertainty Distributions Empirically

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.049241Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9cf68ac0-de09-4acb-868c-1cd83e9158cd · outbound

This paper cites Inductive confidence machines for regression.

Structure-preserving uncertainty quantification for GENERIC dynamics Inductive confidence machines for regression

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:23.304354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fc8576da-606a-45cf-b035-623fd40c57d2 · outbound

This paper cites Distribution-free predictive inference for regression.Journal of the American Statistical Association, 113(523):1094–1111, 2018.

Structure-preserving uncertainty quantification for GENERIC dynamics Distribution-free predictive inference for regression.Journal of the American Statistical Association, 113(523):1094–1111, 2018

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.115255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ce9a098-9c47-4620-86d3-9d9a4df8eeca · outbound

This paper cites Conformal time-series forecasting.Ad- vances in neural information processing systems, 34:6216–6228, 2021.

Structure-preserving uncertainty quantification for GENERIC dynamics Conformal time-series forecasting.Ad- vances in neural information processing systems, 34:6216–6228, 2021

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:23.219088Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 22bbc026-2960-4199-ae16-85160e144e79 · outbound

This paper cites Copula Conformal prediction for multi-step time series prediction.

Structure-preserving uncertainty quantification for GENERIC dynamics Copula Conformal prediction for multi-step time series prediction

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:23.202104Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 21f7dbc1-020f-46d0-af32-1514e49a1557 · outbound

This paper cites Uncertainty quantification of surrogate models using conformal predic- tion.Machine Learning: Science and Technology, 7(1):015025, 2026.

Structure-preserving uncertainty quantification for GENERIC dynamics Uncertainty quantification of surrogate models using conformal predic- tion.Machine Learning: Science and Technology, 7(1):015025, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:23.059847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ea406cb5-4578-40ce-b22d-adeef4e208b1 · outbound

This paper cites A tutorial on conformal prediction.Journal of machine learning research, 9(3), 2008.

Structure-preserving uncertainty quantification for GENERIC dynamics A tutorial on conformal prediction.Journal of machine learning research, 9(3), 2008

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a58b157-6f39-45e4-8799-034941f46f38 · outbound

This paper cites Strictly proper scoring rules, prediction, and estimation.Journal of the American statistical Association, 102(477):359–378, 2007.

Structure-preserving uncertainty quantification for GENERIC dynamics Strictly proper scoring rules, prediction, and estimation.Journal of the American statistical Association, 102(477):359–378, 2007

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.139027Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04f1ee20-391d-4de0-8857-e16a674c7606 · outbound

This paper cites Scoring rules for continuous probability distributions.Management science, 22(10):1087–1096, 1976.

Structure-preserving uncertainty quantification for GENERIC dynamics Scoring rules for continuous probability distributions.Management science, 22(10):1087–1096, 1976

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e02a04c6-9fb2-4405-a769-c1246ef5089d · outbound

This paper cites Decomposition of the continuous ranked probability score for ensemble prediction systems.

Structure-preserving uncertainty quantification for GENERIC dynamics Decomposition of the continuous ranked probability score for ensemble prediction systems

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:22.973345Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 837229c7-35a5-4299-9300-1dda3c62d6e4 · outbound

This paper cites Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds.

Structure-preserving uncertainty quantification for GENERIC dynamics Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:22.869233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7bab3452-7d4d-4de1-91a4-20c1b7ef359e · outbound

This paper cites Courier Corporation, 2008.

Structure-preserving uncertainty quantification for GENERIC dynamics Courier Corporation, 2008

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.337129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aed06791-d498-4a79-aee1-c77e6f33bbfb · outbound

This paper cites Springer, 1998.

Structure-preserving uncertainty quantification for GENERIC dynamics Springer, 1998

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.341630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ba43323-1726-4cb4-979c-670cad75798a · outbound

This paper cites Formulation of thermoelastic dissipative material behavior using GENERIC.Continuum Mechanics and Thermodynamics, 23(3):233–256, 2011.

Structure-preserving uncertainty quantification for GENERIC dynamics Formulation of thermoelastic dissipative material behavior using GENERIC.Continuum Mechanics and Thermodynamics, 23(3):233–256, 2011

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.345484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 607e330b-18d7-4e0c-b49a-69331411879d · outbound

This paper cites John Wiley & Sons, 2009.

Structure-preserving uncertainty quantification for GENERIC dynamics John Wiley & Sons, 2009

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:22.755023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 65946855-fce5-428a-b9bf-5d8f2bb2eee4 · outbound

This paper cites Chapman and Hall/CRC, 2006.

Structure-preserving uncertainty quantification for GENERIC dynamics Chapman and Hall/CRC, 2006

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b2912a9b-f433-4466-9d91-d83dade36049 · outbound

This paper cites Errors-in-variables methods in system identification.Automatica, 43(6):939–958, 2007.

Structure-preserving uncertainty quantification for GENERIC dynamics Errors-in-variables methods in system identification.Automatica, 43(6):939–958, 2007

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 98e4a38b-7727-4195-a64d-6785567e15a4 · outbound

This paper cites an unresolved cited work.

Structure-preserving uncertainty quantification for GENERIC dynamics Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-16T00:13:22.442406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 143ffe6b-8054-424d-ae87-784f5f38d941 · outbound

This paper cites Approximate thompson sampling via epistemic neural networks.

Structure-preserving uncertainty quantification for GENERIC dynamics Approximate thompson sampling via epistemic neural networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:22.371369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation afe9c603-08bd-4526-b17e-fe3b3ebd02b9 · outbound

This paper cites VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification.Neural Networks, page 108543, 2026.

Structure-preserving uncertainty quantification for GENERIC dynamics VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification.Neural Networks, page 108543, 2026

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e42e5e1b-d2a6-4477-9a4b-d9ed0271f11d · outbound

This paper cites Probabilistic forecasts, calibration and sharpness.

Structure-preserving uncertainty quantification for GENERIC dynamics Probabilistic forecasts, calibration and sharpness

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T00:13:22.209489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 89078566-8b79-4231-be4a-8afcfd8de8b6 · outbound

This paper cites Conformal prediction interval for dynamic time-series.

Structure-preserving uncertainty quantification for GENERIC dynamics Conformal prediction interval for dynamic time-series

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Resolution: unresolved; no resolver link, observed 2026-08-16T00:13:21.659505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a155d138-2560-4afc-a659-ac5546e89227 · outbound

This paper cites Multi-element generalized polynomial chaos for arbitrary proba- bility measures.SIAM Journal on Scientific Computing, 28(3):901–928, 2006.

Structure-preserving uncertainty quantification for GENERIC dynamics Multi-element generalized polynomial chaos for arbitrary proba- bility measures.SIAM Journal on Scientific Computing, 28(3):901–928, 2006

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d7c491d9-4701-43c0-9525-364774d83a9c · outbound

This paper cites On decompositions of the KdV 2-soliton.Journal of Nonlinear Science, 16(2):179–200, 2006.

Structure-preserving uncertainty quantification for GENERIC dynamics On decompositions of the KdV 2-soliton.Journal of Nonlinear Science, 16(2):179–200, 2006

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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