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

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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

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This paper cites and Zabaras, N.

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

Reference 2

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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

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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

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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

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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

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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

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

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

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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

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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

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

Reference 12

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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

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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

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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

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

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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

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This paper cites Deep Learning.

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

Reference 18

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

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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

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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

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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

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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

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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

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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

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

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

Reference 27

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

Reference 28

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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

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This paper cites and Zhe, S.

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

Reference 30

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

Reference 31

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

Reference 32

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

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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

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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

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

Reference 36

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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

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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

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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

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

Reference 40

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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

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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

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

Reference 43

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

Reference 44

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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

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

Reference 46

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

Reference 47

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

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

Reference 49

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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

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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

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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

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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

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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

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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

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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

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