Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model | Paper Citation Record

Observation b15ef6d8-fb77-40b3-8b90-2e703c77f2d1 · outbound

This paper cites Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Density physics-informed neural networks reveal sources of cell heterogeneity in signal transduction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.562543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d1c8368d-5094-49ea-8a21-cd5c0696e05d · outbound

This paper cites Random coefficient models for time-series—cross-section data: Monte carlo experi- ments,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Random coefficient models for time-series—cross-section data: Monte carlo experi- ments,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.553938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ed2fc7f0-1942-4cdb-b343-047916fca477 · outbound

This paper cites Modeling dynamics in time-series–cross-section political economy data,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Modeling dynamics in time-series–cross-section political economy data,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.544266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 111fda26-bff4-4fc5-9685-121930ea8405 · outbound

This paper cites Pan, Repeated Cross-Sectional Design.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Pan, Repeated Cross-Sectional Design

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.535293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 40c3c803-954f-41d4-bff1-802840ef83e4 · outbound

This paper cites an unresolved cited work.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:22:24.526338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 43a16a01-c96c-4a59-ab60-b4569d85cbde · outbound

This paper cites Bryman, Social research methods.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Bryman, Social research methods

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.517508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5ee7f2c4-8081-47d7-9cc9-f6fef2c766f0 · outbound

This paper cites Systematic modeling-driven experiments identify distinct molecular clockworks un- derlying hierarchically organized pacemaker neurons,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Systematic modeling-driven experiments identify distinct molecular clockworks un- derlying hierarchically organized pacemaker neurons,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.508728Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c2ed587e-88a8-408d-bf9a-4c024c6aae35 · outbound

This paper cites The radiosensitizer onalespib increases complete remis- sion in 177 lu-dotatate-treated mice bearing neuroen- docrine tumor xenografts,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model The radiosensitizer onalespib increases complete remis- sion in 177 lu-dotatate-treated mice bearing neuroen- docrine tumor xenografts,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.500021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 12ace292-800f-47f8-86bc-1015f537ed80 · outbound

This paper cites Men, women and the dynamics of presidential approval,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Men, women and the dynamics of presidential approval,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.490256Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 84c1df5c-b12a-46f6-add2-06ba3502d2c9 · outbound

This paper cites Whose economy? perceptions of national economic performance during unequal growth,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Whose economy? perceptions of national economic performance during unequal growth,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.480098Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1dd182ba-73fa-4de3-ad05-6c60fde3c799 · outbound

This paper cites Strategic party government: Party influence in congress, 1789– 2000,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Strategic party government: Party influence in congress, 1789– 2000,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.470698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c1583442-51cb-4b8b-9996-510e5be9fdbd · outbound

This paper cites an unresolved cited work.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:22:24.461517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6135aee2-fc2b-4040-8a8f-add974d109c5 · outbound

This paper cites an unresolved cited work.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:22:24.451588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 99e8c2e3-5aa2-4b57-8744-11ce1fe2de73 · outbound

This paper cites Estimating the distribution of parameters in differential equations with repeated cross-sectional data,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Estimating the distribution of parameters in differential equations with repeated cross-sectional data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.442348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 52369708-b561-4617-b980-869767302686 · outbound

This paper cites Host-pathogen kinetics during influenza infection and coinfection: insights from predictive mod- eling,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Host-pathogen kinetics during influenza infection and coinfection: insights from predictive mod- eling,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.432268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fa40f358-d749-47a2-a263-a7f7fb5e85f3 · outbound

This paper cites Parameter and uncertainty estimation for dynamical systems using surrogate stochastic processes,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Parameter and uncertainty estimation for dynamical systems using surrogate stochastic processes,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.421759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ed9eb884-cd48-46d5-bc52-ec68b125a0b8 · outbound

This paper cites Improved most likely het- eroscedastic gaussian process regression via bayesian residual moment estimator,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Improved most likely het- eroscedastic gaussian process regression via bayesian residual moment estimator,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.412189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation eeeadfa4-65b5-4109-ad9e-219f4336822b · outbound

This paper cites hetgp: Heteroskedastic gaussian process modeling and sequential design in r,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model hetgp: Heteroskedastic gaussian process modeling and sequential design in r,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.401966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 717f8116-2fe6-4311-9931-f1d4a84c1ea0 · outbound

This paper cites Markov chain monte carlo without likelihoods,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Markov chain monte carlo without likelihoods,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.392417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d694f88c-8764-487d-bd65-846373dc3cb5 · outbound

This paper cites The frontier of simulation-based inference,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model The frontier of simulation-based inference,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.382984Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation da8bad6d-3417-4eb1-9602-ebba41ffe750 · outbound

This paper cites Equation of state calculations by fast computing machines,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Equation of state calculations by fast computing machines,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.869207Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7df70adc-753f-4e39-853d-f5618840eab1 · outbound

This paper cites Monte carlo sampling methods using markov chains and their applications,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Monte carlo sampling methods using markov chains and their applications,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.369028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 32a732ea-b368-4099-b8ab-c2de9335cb37 · outbound

This paper cites On the importance of the jacobian determinant in parameter inference for random parameter and random measurement error models,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model On the importance of the jacobian determinant in parameter inference for random parameter and random measurement error models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.360313Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ac556f0d-b7d3-40af-a4d6-c149cc189be0 · outbound

This paper cites Hyper- pinn: Learning parameterized differential equations with physics-informed hypernetworks,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Hyper- pinn: Learning parameterized differential equations with physics-informed hypernetworks,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.351368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bfc7e3ba-92be-4846-a9fc-7ef89b9bd9d7 · outbound

This paper cites Wasserstein generative adversarial networks,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Wasserstein generative adversarial networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.342078Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 15109471-8106-4c9b-88be-896bea2a6ce5 · outbound

This paper cites Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.333080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6dfa1b98-3496-4116-9582-69a7413dee75 · outbound

This paper cites The deep ritz method: a deep learning- based numerical algorithm for solving variational prob- lems,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model The deep ritz method: a deep learning- based numerical algorithm for solving variational prob- lems,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.323259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 8de0d55a-0b56-4808-a3a9-9aa625da288e · outbound

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

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Physics- informed neural networks: A deep learning framework for solving forward and inverse problems involving non- linear partial differential equations,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.314131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 790f6b07-7bfb-4e54-9808-bbaa2c60cbe1 · outbound

This paper cites Deepsdf: Learning continuous signed dis- tance functions for shape representation,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Deepsdf: Learning continuous signed dis- tance functions for shape representation,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.304731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df04e7b1-922d-4ae1-ba66-b7965365071f · outbound

This paper cites Learning implicit fields for generative shape modeling,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Learning implicit fields for generative shape modeling,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.295419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation befc5178-3343-4a30-ad99-5db523dd48e1 · outbound

This paper cites Occupancy networks: Learning 3d re- construction in function space,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Occupancy networks: Learning 3d re- construction in function space,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.286348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 9bb5a680-6d53-41b0-8534-2bf5dd664c57 · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Learning nonlinear operators via deeponet based on the universal approximation theorem of operators,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.904273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b070074-9028-4c72-b588-0e792c51d637 · outbound

This paper cites Shift-deeponet: Extending deep operator networks for discontinuous output functions,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Shift-deeponet: Extending deep operator networks for discontinuous output functions,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.271400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c5dc474b-50c7-4bed-b0a1-39155d6bd2b6 · outbound

This paper cites NOMAD: Nonlinear manifold decoders for operator learning,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model NOMAD: Nonlinear manifold decoders for operator learning,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.262208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0f219d6d-691c-479a-a295-f67161eb0dcd · outbound

This paper cites HyperNetworks.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model HyperNetworks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.913708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e129c2c3-8cb3-457e-8085-f8dcfe3d45eb · outbound

This paper cites On the modularity of hyper- networks,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model On the modularity of hyper- networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.252319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f849d538-d81d-4eb2-8f3f-c186c4272953 · outbound

This paper cites HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.920656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e21b3746-c09d-4789-acf8-da17acce46a5 · outbound

This paper cites Generative adversarial nets,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Generative adversarial nets,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.924354Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4daf84f-01e1-49ff-ac6e-f9092140c179 · outbound

This paper cites A framework for data-driven solution and parameter estimation of pdes using conditional generative adversarial networks,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model A framework for data-driven solution and parameter estimation of pdes using conditional generative adversarial networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.237865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation b547e4d9-640f-47df-a0e3-1bac071b1857 · outbound

This paper cites Solution of physics-based bayesian inverse problems with deep gen- erative priors,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Solution of physics-based bayesian inverse problems with deep gen- erative priors,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.228231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 29596d16-1418-4120-ae96-c05d4783a6d9 · outbound

This paper cites Conditional Generative Adversarial Nets.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Conditional Generative Adversarial Nets

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.933789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87740632-0ab5-464d-9fff-48ede653c722 · outbound

This paper cites GATSBI: Generative Adversarial Training for Simulation-Based Inference.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model GATSBI: Generative Adversarial Training for Simulation-Based Inference

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.937345Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63de18cf-ba3a-4d94-8c94-f3d544596bb9 · outbound

This paper cites Attention Is All You Need.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Attention Is All You Need

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.940893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a7b4dda-896e-482a-a5ed-090d92229c84 · outbound

This paper cites Denoising Diffusion Implicit Models.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Denoising Diffusion Implicit Models

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.944738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22adf437-857d-4fb9-bab5-41d6baa764e0 · outbound

This paper cites All-in-one simulation-based inference.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model All-in-one simulation-based inference

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.948294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1d3e77a-08f5-4f02-9597-6b1f063db00e · outbound

This paper cites Improved training of wasserstein gans,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Improved training of wasserstein gans,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.219143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bae8b820-8fd5-4e7b-91e6-e7b40b6eb7b0 · outbound

This paper cites Villani et al., Optimal transport: old and new.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Villani et al., Optimal transport: old and new

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.210424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bcfa543f-925f-4a45-b116-98146a5d402d · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.958396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78b38edc-e89c-4bed-925b-163cce74a11a · outbound

This paper cites Spatiotem- poral distribution of β-amyloid in alzheimer disease is the result of heterogeneous regional carrying capacities,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Spatiotem- poral distribution of β-amyloid in alzheimer disease is the result of heterogeneous regional carrying capacities,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.201515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fdbc6538-f26b-4463-9e2b-46d027e75479 · outbound

This paper cites Optimal anti- amyloid-beta therapy for alzheimer’s disease via a per- sonalized mathematical model,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Optimal anti- amyloid-beta therapy for alzheimer’s disease via a per- sonalized mathematical model,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.192115Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 367de120-17b1-4123-81ca-b2272afe46f8 · outbound

This paper cites Dgm: A deep learning algorithm for solving partial differential equations,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Dgm: A deep learning algorithm for solving partial differential equations,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.182581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fe269740-c497-4eae-9cdc-c145d5cfc103 · outbound

This paper cites Deep Neural Network Approach to Forward-Inverse Problems.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Deep Neural Network Approach to Forward-Inverse Problems

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-11T00:22:23.971655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dc5e3ff-6e87-455e-8eac-791e1a650f69 · outbound

This paper cites The monotone traveling wave solution of a bistable three-species compe- tition system via unconstrained neural networks,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model The monotone traveling wave solution of a bistable three-species compe- tition system via unconstrained neural networks,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.173273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0c842793-9dcc-4320-9eb7-26ec13723c60 · outbound

This paper cites Physics- informed neural operator for learning partial differential equations,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Physics- informed neural operator for learning partial differential equations,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.164351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 350419b2-6cf1-4183-ab31-993037221deb · outbound

This paper cites an unresolved cited work.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Unresolved cited work

Reference 55

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:22:24.154581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b0ae08b-7bd1-4ea7-bb91-64b46532cfaf · outbound

This paper cites A model of inductive bias learning,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model A model of inductive bias learning,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.144793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 64e0dbf8-40f8-4b1f-9338-a8c06b3667e4 · outbound

This paper cites Simultaneous approximations of multivariate functions and their derivatives by neural networks with one hidden layer,.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Simultaneous approximations of multivariate functions and their derivatives by neural networks with one hidden layer,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.135818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7989c0d6-f844-48da-8f43-24ab4f369ba5 · outbound

This paper cites an unresolved cited work.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-11T00:22:24.126103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ca06794e-585d-44b6-968c-54c2860d072a · outbound

This paper cites Thus, we alterna- tively use the discretized version of Lphysics defined in Eq.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model Thus, we alterna- tively use the discretized version of Lphysics defined in Eq

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T00:22:24.116884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c9eff9f3-4a8d-4e73-8f85-9f7e7326ad1e · outbound

This paper cites In this section, we demonstrate that hyperPINN can effectively minimize Lphysics(disc) by applying the universal approximation theorem for neural networks.

Estimation of System Parameters Including Repeated Cross-Sectional Data through Emulator-Informed Deep Generative Model In this section, we demonstrate that hyperPINN can effectively minimize Lphysics(disc) by applying the universal approximation theorem for neural networks

Reference 60

Resolution: malformed identifier; raw_fallback, observed 2026-08-11T00:22:24.106511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers