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

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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

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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

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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

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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

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

Reference 5

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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

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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

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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

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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

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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

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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

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

Reference 12

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Reference 13

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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

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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

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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

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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

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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

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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

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

Reference 20

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

Reference 30

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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

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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

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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

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

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

Reference 34

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

Reference 35

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

Reference 36

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

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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

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

Reference 38

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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

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

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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

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

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

Reference 41

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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

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

Reference 43

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

Reference 44

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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

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

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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

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

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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

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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

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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