Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks | Paper Citation Record

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This paper cites Physics Informed Neural Net- works for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Physics Informed Neural Net- works for Modeling of 3D Flow-Thermal Problems with Sparse Domain Data

Reference 1

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This paper cites Hemo- dynamics modeling with physics-informed neural networks: A progressive boundary complexity approach.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Hemo- dynamics modeling with physics-informed neural networks: A progressive boundary complexity approach

Reference 2

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This paper cites Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Reference 3

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Eulerian formulation of the tensor-based morphology equations for strain-based blood damage modeling

Reference 4

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This paper cites Application of CFD to Analyze the Hydrodynamic Behaviour of a Biore- actor with a Double Impeller.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Application of CFD to Analyze the Hydrodynamic Behaviour of a Biore- actor with a Double Impeller

Reference 5

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This paper cites Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations

Reference 6

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This paper cites Faroughi, Nikhil M.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Faroughi, Nikhil M

Reference 7

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This paper cites Garcia-Ochoa, V.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Garcia-Ochoa, V

Reference 8

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This paper cites Geometry-aware PINNs for Turbulent Flow Prediction, December 2024.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Geometry-aware PINNs for Turbulent Flow Prediction, December 2024

Reference 9

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This paper cites A physics- informed deep learning framework for inversion and surrogate modeling in solid mechanics.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks A physics- informed deep learning framework for inversion and surrogate modeling in solid mechanics

Reference 10

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Unresolved cited work

Reference 11

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This paper cites Flow field reconstruction from sparse sensor mea- surements with physics-informed neural networks.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Flow field reconstruction from sparse sensor mea- surements with physics-informed neural networks

Reference 12

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This paper cites A General Review of the Current Development of Mechanically Agitated Vessels.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks A General Review of the Current Development of Mechanically Agitated Vessels

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This paper cites NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equa- tions.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks NSFnets (Navier-Stokes Flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equa- tions

Reference 14

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This paper cites Joshi, Nandkishor K.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Joshi, Nandkishor K

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Fotiadis

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This paper cites Lorenzen, A.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Lorenzen, A

Reference 17

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

Reference 18

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks McClenny and Ulisses M

Reference 19

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This paper cites Finite basis physics-informed neural networks (FBPINNs): A scalable domain decomposition approach for solving differential equa- tions.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Finite basis physics-informed neural networks (FBPINNs): A scalable domain decomposition approach for solving differential equa- tions

Reference 20

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Raissi, P

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Hidden fluid me- chanics: Learning velocity and pressure fields from flow visualizations

Reference 22

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This paper cites CFD simulation of a Rushton turbine stirred-tank using open-source software with critical evaluation of MRF-based rotation modeling.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks CFD simulation of a Rushton turbine stirred-tank using open-source software with critical evaluation of MRF-based rotation modeling

Reference 23

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This paper cites Rosseburg, J.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Rosseburg, J

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks PFNN: A penalty-free neural network method for solving a class of second-order boundary-value problems on complex geometries

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Jagtap, and George Em Karniadakis

Reference 26

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This paper cites Steinfurth, A.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Steinfurth, A

Reference 27

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Shape-optimization of extrusion-dies via parameterized physics-informed neural networks

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This paper cites A model hierarchy for predicting the flow in stirred tanks with physics-informed neu- ral networks.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks A model hierarchy for predicting the flow in stirred tanks with physics-informed neu- ral networks

Reference 29

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks PACMANN: Point Adaptive Collocation Method for Artificial Neural Networks, November 2024

Reference 30

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Understanding and Mitigating Gradient Flow Pathologies in Physics-Informed Neural Networks

Reference 31

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks An Expert's Guide to Training Physics-informed Neural Networks

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Gradient Alignment in Physics-informed Neural Networks: A Second-Order Optimization Perspective, February 2025

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks A comprehensive study of non- adaptive and residual-based adaptive sampling for physics-informed neural networks

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Hy- brid Modeling of Fed-Batch Cell Culture Using Physics-Informed Neural Network

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks MultiAdam: Parameter-wise scale-invariant optimizer for multiscale training of physics-informed neural net- works

Reference 36

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Non-intrusive reduced-order modeling for fluid prob- lems: A brief review

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Gambaruto

Reference 38

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This paper cites Physics-Informed Neural Networks with Complementary Soft and Hard Constraints for Solving Complex Boundary Navier-Stokes Equations, November 2024.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Physics-Informed Neural Networks with Complementary Soft and Hard Constraints for Solving Complex Boundary Navier-Stokes Equations, November 2024

Reference 39

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks doi: 10.1109/72.712178

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks doi: 10.1016/j.jcp.2021.110683

Reference 2021

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This paper cites doi: 10.1002/pamm.202300203.

Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks doi: 10.1002/pamm.202300203

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Unresolved cited work

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Quantifying data needs in surrogate modeling for flow fields in two-dimensional stirred tanks with physics-informed neural networks Unresolved cited work

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