Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems | Paper Citation Record

Observation c08c6dee-99bd-4696-baad-0f0b4bc0cd80 · outbound

This paper cites M., Chung, J., and Chung, M.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems M., Chung, J., and Chung, M

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.527376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 280a0cac-2ab3-4477-afbd-ad4ebab39eeb · outbound

This paper cites and Zabaras, N.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems and Zabaras, N

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T12:16:14.142199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d68cd644-6f00-410d-b76a-02f5d41912a3 · outbound

This paper cites An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks

Reference 3

Resolution: metadata mismatch; local_arxiv, observed 2026-08-10T12:16:14.041535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9458c513-458c-445a-8f64-a107c20ba75c · outbound

This paper cites C., Onwunta, A., and Verma, D.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems C., Onwunta, A., and Verma, D

Reference 4

Resolution: verified exact; doi, observed 2026-08-10T12:16:13.124494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e17065af-1159-4a7f-a274-a757d835f114 · outbound

This paper cites Implicit regularization for deep neural networks driven by an ornstein-uhlenbeck like process, 2020.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Implicit regularization for deep neural networks driven by an ornstein-uhlenbeck like process, 2020

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.510687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 325a619b-8ea9-4238-ad14-0bcdb97ad9d4 · outbound

This paper cites Convergence properties of a class of quasi-newton methods in optimization.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Convergence properties of a class of quasi-newton methods in optimization

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.495185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fe862704-51b5-4a7c-ad6d-60868111a34f · outbound

This paper cites Analysis of explainers of black box deep neural networks for computer vision: A survey.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Analysis of explainers of black box deep neural networks for computer vision: A survey

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.592223Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c465127b-5716-4683-ac60-9384ab4ea4f5 · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.597252Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 391eb377-7416-45b8-9bf1-9517d7b7c41f · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.601652Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 897dbd7e-dfd4-483d-a1b0-cee75e7e8133 · outbound

This paper cites Neural networks for quantum inverse problems.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Neural networks for quantum inverse problems

Reference 10

Resolution: verified exact; doi, observed 2026-08-10T12:16:13.082879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 453e5b1d-5f3e-45ca-921e-446f87e2bd1e · outbound

This paper cites Inversion of Integral Models: a Neural Network Approach.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Inversion of Integral Models: a Neural Network Approach

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-10T12:16:13.065499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation aef1e71f-b4e7-4d3e-9678-b12ccd855fae · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:16:14.469339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ec131e0b-96d9-4bf8-b4e9-8fc7469c781b · outbound

This paper cites DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems DeepCFD: Efficient Steady-State Laminar Flow Approximation with Deep Convolutional Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.619721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03790c61-ed58-4ac0-9298-e41628fd376a · outbound

This paper cites Regularization by architecture: A deep prior approach for inverse problems.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Regularization by architecture: A deep prior approach for inverse problems

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.453861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f41d1c54-9eee-4110-aa8f-ad95db4a59e9 · outbound

This paper cites Solving inverse problems in steady-state navier-stokes equations using deep neural networks, 2020.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Solving inverse problems in steady-state navier-stokes equations using deep neural networks, 2020

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.438911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e4e6f220-1e68-47bb-b182-42c9cd9816bd · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.633855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f3281e7-8ea0-420b-b781-ecc81f8eda42 · outbound

This paper cites Solving Inverse Problems With Deep Neural Networks -- Robustness Included?.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Solving Inverse Problems With Deep Neural Networks -- Robustness Included?

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-10T12:16:13.019945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e2503da2-9115-4951-af28-9afea69cbd39 · outbound

This paper cites Deep Learning.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Deep Learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.643643Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e3383c3-1d44-4a99-acc2-09441792877c · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:16:14.414699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 36f4d602-7218-43a8-a39e-98a6b2b8c154 · outbound

This paper cites Convolutional neural networks for steady flow approximation.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Convolutional neural networks for steady flow approximation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.653294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30b123ce-041a-4a94-8c70-a892de01803f · outbound

This paper cites phiflow: A differentiable pde solving framework for deep learning via physical simulations.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems phiflow: A differentiable pde solving framework for deep learning via physical simulations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.399952Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d352a487-9995-45e6-b586-9433fa470c4f · outbound

This paper cites The partial differential equation.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems The partial differential equation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.383946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fa8904e3-e3d1-4406-94f4-40c47882aa93 · outbound

This paper cites Difftaichi: Differentiable programming for physical simulation, 2020.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Difftaichi: Differentiable programming for physical simulation, 2020

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.369839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b50b4a3-5f2e-48bc-aef2-38be1532f4d6 · outbound

This paper cites Inverse problems in atmospheric science and their application.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Inverse problems in atmospheric science and their application

Reference 24

Resolution: verified exact; doi, observed 2026-08-10T12:16:12.995110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9e2646c4-2272-4bae-bbc6-e9191e004154 · outbound

This paper cites D., Mao, Z., Adams, N., and Karniadakis, G.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems D., Mao, Z., Adams, N., and Karniadakis, G

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.676672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50b0446c-466e-47c7-9e51-15a2c256a2e3 · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.681677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7c5d6f5-792f-4b6f-b437-2d3890a31439 · outbound

This paper cites Diffusion-Induced Chaos in Reaction Systems.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Diffusion-Induced Chaos in Reaction Systems

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.686639Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99625819-2383-4251-8a94-0ad5ee600e32 · outbound

This paper cites Crafting papers on machine learning.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Crafting papers on machine learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.692423Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9873938b-ca22-443f-b1ee-ce4409be2f4d · outbound

This paper cites Surrogate modeling for bayesian inverse problems based on physics-informed neural networks.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Surrogate modeling for bayesian inverse problems based on physics-informed neural networks

Reference 29

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T12:16:13.830652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c82af36d-2507-4869-b922-3ea0e3a5dd1c · outbound

This paper cites and Zhe, S.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems and Zhe, S

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.334031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cb8b6c5d-79f4-4c8d-b38c-cd63039b8f43 · outbound

This paper cites D., and Karniadakis, G.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems D., and Karniadakis, G

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.706706Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f037c62-f90b-45f9-95d4-e1613c5c6fca · outbound

This paper cites T., Jin, K.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems T., Jin, K

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.711796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f4a6ccb8-7181-49c7-80ab-f7c7019a4015 · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.716257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 22193080-3749-429d-be79-b660959e7840 · outbound

This paper cites Regularization, bayesian inference, and machine learning methods for inverse problems.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Regularization, bayesian inference, and machine learning methods for inverse problems

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.306844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2c76db5a-4059-4cf5-b1b5-996c1e7c64a1 · outbound

This paper cites A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations

Reference 35

Resolution: verified exact; local_arxiv, observed 2026-08-10T12:16:13.604945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation aff39039-3f90-42a5-a623-118113f0f361 · outbound

This paper cites and Srinivasan, B.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems and Srinivasan, B

Reference 36

Resolution: verified exact; doi, observed 2026-08-10T12:16:12.956510Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be61048b-8f84-47ab-8157-40f911c6fa94 · outbound

This paper cites A surrogate optimization approach for inverse problems: Application to turbulent mixed-convection flows.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems A surrogate optimization approach for inverse problems: Application to turbulent mixed-convection flows

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.736705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3a4143c-61e4-4a38-88f1-9a9e085f3db0 · outbound

This paper cites Optimisation of manufacturing process parameters using deep neural networks as surrogate models.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Optimisation of manufacturing process parameters using deep neural networks as surrogate models

Reference 38

Resolution: verified exact; doi, observed 2026-08-10T12:16:12.940485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 514520ef-ce5f-4722-9617-0accd7574262 · outbound

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

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.746765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b5266ea-359a-4640-9130-3ed94c9ab1dc · outbound

This paper cites Solving physics-based inverse problems using gans.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Solving physics-based inverse problems using gans

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.286607Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation fc90dd52-741c-40da-93d2-3eb5ea443900 · outbound

This paper cites Physics-informed neural network for seismic wave inversion in layered semi-infinite domain, 2023.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Physics-informed neural network for seismic wave inversion in layered semi-infinite domain, 2023

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.265776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8436b144-2e17-4414-ba5b-725ecd708172 · outbound

This paper cites Benchmarking deep inverse models over time, and the neural-adjoint method, 2021.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Benchmarking deep inverse models over time, and the neural-adjoint method, 2021

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.250258Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0d8f3149-e464-4d93-b1d0-9e6b9d890ee2 · outbound

This paper cites An overview of gradient descent optimization algorithms, 2017.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems An overview of gradient descent optimization algorithms, 2017

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.234818Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9546dbd4-0b08-4506-8c32-ec29f534ffb4 · outbound

This paper cites E., and Kuhl, E.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems E., and Kuhl, E

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.220038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4232c7da-68fa-4ea3-9058-975d181e782d · outbound

This paper cites A comparative study of the explicit finite difference method and physics-informed neural networks for solving the burgers; equation.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems A comparative study of the explicit finite difference method and physics-informed neural networks for solving the burgers; equation

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.774512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b649dc5d-8671-408f-a726-61d81cad4638 · outbound

This paper cites P., and De Freitas, N.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems P., and De Freitas, N

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.779562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29d894b2-2247-4c4c-ba21-55841d7e79d8 · outbound

This paper cites Surrogate modeling for porous flow using deep neural networks.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Surrogate modeling for porous flow using deep neural networks

Reference 47

Resolution: metadata mismatch; raw_fallback, observed 2026-08-10T12:16:13.470599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6a645753-9ef9-4e5d-b924-8e5f9c392b13 · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.788533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e834b244-48f6-47b7-ba9c-d93feb63ac5d · outbound

This paper cites an unresolved cited work.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-10T12:16:14.192364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d6d8e74b-5623-4cd1-85c8-45636e9068f9 · outbound

This paper cites Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.799090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecd6f543-c71d-40c9-9ede-92c3293b6f92 · outbound

This paper cites Convpde-uq: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Convpde-uq: Convolutional neural networks with quantified uncertainty for heterogeneous elliptic partial differential equations on varied domains

Reference 51

Resolution: verified exact; doi, observed 2026-08-10T12:16:12.904105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5ec98111-e4d4-4b66-abaa-4ceeac635ba9 · outbound

This paper cites Using cnns to optimize numerical simulations in geotechnical engineering.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Using cnns to optimize numerical simulations in geotechnical engineering

Reference 52

Resolution: verified exact; doi, observed 2026-08-10T12:16:12.887318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1f50844a-6d4b-4858-ba97-5f96d7d1c45f · outbound

This paper cites K., and Vesselinov, V.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems K., and Vesselinov, V

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.176152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 74e79f28-c8b6-4499-a6a1-9853edf49ec3 · outbound

This paper cites Physics-informed neural networks for data-free surrogate modelling and engineering optimization – an example from composite manufacturing.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Physics-informed neural networks for data-free surrogate modelling and engineering optimization – an example from composite manufacturing

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-10T12:16:12.819677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3814173-4514-4126-846d-a8b57ed60503 · outbound

This paper cites B., and Wetzstein, G.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems B., and Wetzstein, G

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T12:16:14.158781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 156fabdc-a15b-477e-8ae2-3cb960bf21f6 · outbound

This paper cites Surrogate-based physics-informed neural networks for elliptic partial differential equations.

Optimization Landscapes Learned: Proxy Networks Boost Convergence in Physics-based Inverse Problems Surrogate-based physics-informed neural networks for elliptic partial differential equations

Reference 56

Resolution: verified exact; doi, observed 2026-08-10T12:16:12.869894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers