Physics-Constrained Machine Learning for Chemical Engineering | Paper Citation Record

Observation fae8e4f9-eaa4-44d7-95f4-1e8625305c43 · outbound

This paper cites Physics- constrained deep learning for high-dimensional surrogate modeling and uncertainty quantifi- cation without labeled data.

Physics-Constrained Machine Learning for Chemical Engineering Physics- constrained deep learning for high-dimensional surrogate modeling and uncertainty quantifi- cation without labeled data

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.539034Z

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Observation 5d0dbf6e-49a8-4d48-8c9c-f304b8f9d101 · outbound

This paper cites Raissi, P.

Physics-Constrained Machine Learning for Chemical Engineering Raissi, P

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.531163Z

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Observation fc78d6b4-3e26-4825-ba07-184867dbb5da · outbound

This paper cites Scientific machine learning through physics–informed neural networks: Where we are and what’s next.

Physics-Constrained Machine Learning for Chemical Engineering Scientific machine learning through physics–informed neural networks: Where we are and what’s next

Reference 3

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Observation 76ad6fb4-57e0-4428-8c4d-03848b5b7450 · outbound

This paper cites When physics meets machine learning: a survey of physics-informed machine learning.

Physics-Constrained Machine Learning for Chemical Engineering When physics meets machine learning: a survey of physics-informed machine learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.519037Z

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Observation 58933285-c1d3-41e2-a048-7606df2982bf · outbound

This paper cites A novel temperature prediction method without using energy equation based on physics-informed neural network (pinn): A case study on plate- circular/square pin-fin heat sinks.

Physics-Constrained Machine Learning for Chemical Engineering A novel temperature prediction method without using energy equation based on physics-informed neural network (pinn): A case study on plate- circular/square pin-fin heat sinks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.511629Z

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Observation de1e2773-edb1-4981-b0b2-61011c345614 · outbound

This paper cites an unresolved cited work.

Physics-Constrained Machine Learning for Chemical Engineering Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:00:22.504010Z

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Observation 96b6a6d9-ae47-486f-aaf7-59ee34a94a96 · outbound

This paper cites Development of steady-state and dynamic mass and energy constrained neural networks for distributed chemical systems using noisy transient data.

Physics-Constrained Machine Learning for Chemical Engineering Development of steady-state and dynamic mass and energy constrained neural networks for distributed chemical systems using noisy transient data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.496646Z

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Observation 02f57147-f6cb-4846-adeb-0ad019248546 · outbound

This paper cites Lueg, Victor Alves, Daniel Schicksnus, John R.

Physics-Constrained Machine Learning for Chemical Engineering Lueg, Victor Alves, Daniel Schicksnus, John R

Reference 8

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Observation 1c1ec400-52a4-4d87-8581-fb50c17175fb · outbound

This paper cites Jakobsen.

Physics-Constrained Machine Learning for Chemical Engineering Jakobsen

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.489418Z

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Observation 2c709602-85bc-4a02-8a7e-32e6a0797fd3 · outbound

This paper cites Development of mass, energy, and thermo- dynamics constrained steady-state and dynamic neural networks for interconnected chemical systems.

Physics-Constrained Machine Learning for Chemical Engineering Development of mass, energy, and thermo- dynamics constrained steady-state and dynamic neural networks for interconnected chemical systems

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.482214Z

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Observation b538f365-d830-4913-a570-9d9d6dde39ee · outbound

This paper cites Constante Flores, and Can Li.

Physics-Constrained Machine Learning for Chemical Engineering Constante Flores, and Can Li

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.473967Z

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Observation 9ecf733d-596e-4a70-91dc-71ba04a200c1 · outbound

This paper cites ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection.

Physics-Constrained Machine Learning for Chemical Engineering ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T15:00:21.832352Z

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Observation 44ed2992-fadb-44e0-b45d-5c36eb7ac6ca · outbound

This paper cites Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks.

Physics-Constrained Machine Learning for Chemical Engineering Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T15:00:21.835034Z

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Observation 442c8612-daa6-4579-afe9-88dff8ba7452 · outbound

This paper cites Hardnet: Hard-constrained neural networks with universal approximation guarantees.

Physics-Constrained Machine Learning for Chemical Engineering Hardnet: Hard-constrained neural networks with universal approximation guarantees

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T15:00:21.837764Z

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Observation dd1250b4-ce91-4ff2-97a5-1ac0c1033076 · outbound

This paper cites On the development of steady-state and dy- namic mass-constrained neural networks using noisy transient data.

Physics-Constrained Machine Learning for Chemical Engineering On the development of steady-state and dy- namic mass-constrained neural networks using noisy transient data

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.466786Z

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Observation 666d5f6c-74a3-4ad8-8dae-a625e0361ca3 · outbound

This paper cites Enforcing analytic constraints in neural networks emulating physical systems.

Physics-Constrained Machine Learning for Chemical Engineering Enforcing analytic constraints in neural networks emulating physical systems

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.459049Z

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Observation 40a9ec72-a7d9-47d9-a8d0-2785a64d6826 · outbound

This paper cites Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints.

Physics-Constrained Machine Learning for Chemical Engineering Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-05T15:00:21.844489Z

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Observation 3a56497a-2205-4c03-b3d8-e40b464a0753 · outbound

This paper cites an unresolved cited work.

Physics-Constrained Machine Learning for Chemical Engineering Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:00:22.451499Z

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Observation 6758d4b1-1948-4f02-abc7-329a1936262d · outbound

This paper cites An overview of simultaneous strategies for dynamic optimization.

Physics-Constrained Machine Learning for Chemical Engineering An overview of simultaneous strategies for dynamic optimization

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.444298Z

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Observation 0a895fda-df82-471c-b21b-062a78385a54 · outbound

This paper cites Neural networks meet physics: A survey of physics-informed approaches to modeling and simulation.

Physics-Constrained Machine Learning for Chemical Engineering Neural networks meet physics: A survey of physics-informed approaches to modeling and simulation

Reference 20

Resolution: metadata mismatch; raw_fallback, observed 2026-08-05T15:00:22.070132Z

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Observation f1270c55-9c14-4d8e-acf6-0ffc947664a4 · outbound

This paper cites B-pinns: Bayesian physics-informed neural networks for forward and inverse pde problems with noisy data.Journal of Computational Physics, 425:109913, 2021.

Physics-Constrained Machine Learning for Chemical Engineering B-pinns: Bayesian physics-informed neural networks for forward and inverse pde problems with noisy data.Journal of Computational Physics, 425:109913, 2021

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T15:00:21.854144Z

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Observation 8af976ad-e2dd-4e03-85a1-088a4ba71fcb · outbound

This paper cites fpinns: Fractional physics-informed neural networks.

Physics-Constrained Machine Learning for Chemical Engineering fpinns: Fractional physics-informed neural networks

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.431058Z

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Observation d6302e04-ac00-4dd2-9cad-7ff04e0a231d · outbound

This paper cites Jagtap, Ehsan Kharazmi, and George Em Karniadakis.

Physics-Constrained Machine Learning for Chemical Engineering Jagtap, Ehsan Kharazmi, and George Em Karniadakis

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.423364Z

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Observation 44b5d165-79c9-4540-83f1-31cedbae6915 · outbound

This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.

Physics-Constrained Machine Learning for Chemical Engineering Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.415683Z

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Observation 0258d80d-65e2-4a4f-8e7f-8770aa5f719a · outbound

This paper cites Perspectives on the integration between first-principles and data-driven modeling.

Physics-Constrained Machine Learning for Chemical Engineering Perspectives on the integration between first-principles and data-driven modeling

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.407199Z

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Observation c94a1327-8c00-4e8c-97bd-4e6608835d94 · outbound

This paper cites Dae-pinn: a physics-informed neural network model for sim- ulating differential algebraic equations with application to power networks.

Physics-Constrained Machine Learning for Chemical Engineering Dae-pinn: a physics-informed neural network model for sim- ulating differential algebraic equations with application to power networks

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.399478Z

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Observation 1a6326d7-e67b-4dac-92f7-96c869c4406c · outbound

This paper cites When and why pinns fail to train: A neural tangent kernel perspective.

Physics-Constrained Machine Learning for Chemical Engineering When and why pinns fail to train: A neural tangent kernel perspective

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T15:00:21.867946Z

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Observation 72adb6a9-1555-4826-ba52-3a2788ab1376 · outbound

This paper cites Physics- constrained neural ordinary differential equation models to discover and predict microbial com- munity dynamics.

Physics-Constrained Machine Learning for Chemical Engineering Physics- constrained neural ordinary differential equation models to discover and predict microbial com- munity dynamics

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.387247Z

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Observation f34ef86e-9459-4500-86dd-000130828413 · outbound

This paper cites Zavala, Carl D.

Physics-Constrained Machine Learning for Chemical Engineering Zavala, Carl D

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.379894Z

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Observation 1da76c9c-f50e-4558-8cbf-6147c1d5f323 · outbound

This paper cites an unresolved cited work.

Physics-Constrained Machine Learning for Chemical Engineering Unresolved cited work

Reference 30

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:00:22.372979Z

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Observation bb186a9b-3888-4c1d-ae61-906a4f18a804 · outbound

This paper cites Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using saccharomyces cerevisiae.

Physics-Constrained Machine Learning for Chemical Engineering Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using saccharomyces cerevisiae

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.366123Z

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Observation 88ac5638-f216-4072-a8f3-eef0cff3b094 · outbound

This paper cites Physics-informed neural networks for heat transfer prediction in two-phase flows.

Physics-Constrained Machine Learning for Chemical Engineering Physics-informed neural networks for heat transfer prediction in two-phase flows

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.358326Z

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Observation b1c4dab7-c757-4a0b-9167-82b76febf600 · outbound

This paper cites A physics-informed assembly of feed- forward neural network engines to predict inelasticity in cross-linked polymers.

Physics-Constrained Machine Learning for Chemical Engineering A physics-informed assembly of feed- forward neural network engines to predict inelasticity in cross-linked polymers

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.351189Z

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Observation 968c88be-fa26-4c4c-8199-f4bb6320041f · outbound

This paper cites cv-pinn: Efficient learning of variational physics-informed neural network with domain decomposition.

Physics-Constrained Machine Learning for Chemical Engineering cv-pinn: Efficient learning of variational physics-informed neural network with domain decomposition

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.343594Z

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Observation 2a2b31af-484b-4ff7-bcdd-f2b2f4c8edb3 · outbound

This paper cites Physics-informed neural networks with domain decomposition for the incompressible navier–stokes equations.

Physics-Constrained Machine Learning for Chemical Engineering Physics-informed neural networks with domain decomposition for the incompressible navier–stokes equations

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.336386Z

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Observation e4500e35-07af-45f0-b869-a70cc3e27f13 · outbound

This paper cites Physics-based neural networks for simulation and synthesis of cyclic adsorption processes.

Physics-Constrained Machine Learning for Chemical Engineering Physics-based neural networks for simulation and synthesis of cyclic adsorption processes

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.329198Z

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Observation 5b45d717-6ed3-4c6a-8fec-25eb90934a4f · outbound

This paper cites Learning the solution operator of parametric partial differential equations with physics-informed deeponets.Science Advances, 7(40):eabi8605, 2021.

Physics-Constrained Machine Learning for Chemical Engineering Learning the solution operator of parametric partial differential equations with physics-informed deeponets.Science Advances, 7(40):eabi8605, 2021

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.321655Z

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Observation 86dd856b-22c7-4719-b07b-8d6a1800f4a9 · outbound

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

Physics-Constrained Machine Learning for Chemical Engineering Physics-informed neural operator for learning partial differential equations

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.314444Z

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Observation cd2b0e7f-2337-4068-a9f3-98be748313bc · outbound

This paper cites an unresolved cited work.

Physics-Constrained Machine Learning for Chemical Engineering Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:00:22.307428Z

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Observation cbf8b466-c799-4711-b351-18bd72459e07 · outbound

This paper cites A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection.

Physics-Constrained Machine Learning for Chemical Engineering A Review of Physics-Informed Machine Learning Methods with Applications to Condition Monitoring and Anomaly Detection

Reference 40

Resolution: verified exact; local_arxiv, observed 2026-08-05T15:00:21.953754Z

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Observation a8b29faa-72a8-441a-a134-8e0e0fe893b4 · outbound

This paper cites Physics-informed recurrent neu- ral network modeling for predictive control of nonlinear processes.

Physics-Constrained Machine Learning for Chemical Engineering Physics-informed recurrent neu- ral network modeling for predictive control of nonlinear processes

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.300026Z

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Observation e38a04bf-5d5f-4109-82f3-e14353ef66a6 · outbound

This paper cites an unresolved cited work.

Physics-Constrained Machine Learning for Chemical Engineering Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-05T15:00:22.292576Z

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Observation 75e922c8-9b51-48f2-bea8-09098f60c53e · outbound

This paper cites Physics-informed deep koopman op- erator for lagrangian dynamic systems.

Physics-Constrained Machine Learning for Chemical Engineering Physics-informed deep koopman op- erator for lagrangian dynamic systems

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.285339Z

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Observation 28c590aa-2acc-4b08-9cee-591b625e1ef6 · outbound

This paper cites Assessment of uncertainty quantification in universal differential equations.

Physics-Constrained Machine Learning for Chemical Engineering Assessment of uncertainty quantification in universal differential equations

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.277808Z

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Observation 47adb641-5e13-4cbe-aaf9-48faf31ed8d8 · outbound

This paper cites Thompson, Victor M.

Physics-Constrained Machine Learning for Chemical Engineering Thompson, Victor M

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.269821Z

Source-reported events for the cited work

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Observation 00864643-5570-4dbb-b24c-c0c1374cda0c · outbound

This paper cites Deepxde: A deep learning library for solving differential equations.

Physics-Constrained Machine Learning for Chemical Engineering Deepxde: A deep learning library for solving differential equations

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.262199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c9a2dbb-8994-48d4-966b-eb334f54b996 · outbound

This paper cites Learning constrained parametric differentiable predictive control policies with guarantees.

Physics-Constrained Machine Learning for Chemical Engineering Learning constrained parametric differentiable predictive control policies with guarantees

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.254496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f22fec19-1249-4482-846f-6e8ffbdfc61c · outbound

This paper cites Continuous-molecular targeting for integrated solvent and process design.

Physics-Constrained Machine Learning for Chemical Engineering Continuous-molecular targeting for integrated solvent and process design

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.246528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 53c61152-7081-4310-88b9-8c6adeaa6bf0 · outbound

This paper cites Ikegwu, Panzheng Zhou, Reid C.

Physics-Constrained Machine Learning for Chemical Engineering Ikegwu, Panzheng Zhou, Reid C

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:00:22.238656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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