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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation

As of 9 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.08277.

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2507.08277 v2

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measured 46 of 46 reference resolution

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46 of 46 outbound references displayed

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Outbound references

Observation 2357cabb-2f89-4b63-a4fe-09f9c1bb453e · outbound

This paper cites Ammonia for power.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Ammonia for power

Reference 1

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This paper cites Review on Ammonia as a Potential Fuel: From Synthesis to Economics.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Review on Ammonia as a Potential Fuel: From Synthesis to Economics

Reference 2

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This paper cites Ammonia as a hydrogen energy carrier.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Ammonia as a hydrogen energy carrier

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This paper cites A review of ammonia as a compression ignition engine fuel.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation A review of ammonia as a compression ignition engine fuel

Reference 4

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Observation 6dfa9851-fc56-4ca9-b194-df127b54135d · outbound

This paper cites Unlocking ammonia engines: pre-chamber ignition with partial ammonia cracking.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Unlocking ammonia engines: pre-chamber ignition with partial ammonia cracking

Reference 5

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This paper cites A review on clean ammonia as a potential fuel for power generators.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation A review on clean ammonia as a potential fuel for power generators

Reference 6

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This paper cites Ammonia as Effective Hydrogen Storage: A Review on Production, Storage and Utilization.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Ammonia as Effective Hydrogen Storage: A Review on Production, Storage and Utilization

Reference 7

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This paper cites Emission and Combustion Characteristics of Ammonia/Methane Mixtures for Carbon Reduction and Alternative Fuel Development.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Emission and Combustion Characteristics of Ammonia/Methane Mixtures for Carbon Reduction and Alternative Fuel Development

Reference 8

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This paper cites Effects of injection pa- rameters on the combustion characteristics of ammonia-diesel dual-fuel direct-injection (ADDI) mode and combustion enhancement at high ammonia energy ratio.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Effects of injection pa- rameters on the combustion characteristics of ammonia-diesel dual-fuel direct-injection (ADDI) mode and combustion enhancement at high ammonia energy ratio

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This paper cites Renew and Sustain Energy Rev.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Renew and Sustain Energy Rev

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This paper cites Propagation and emissions of premixed methane-ammonia/air flames.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Propagation and emissions of premixed methane-ammonia/air flames

Reference 11

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This paper cites Toward accommodating realistic fuel chemistry in large- scale computations.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Toward accommodating realistic fuel chemistry in large- scale computations

Reference 12

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This paper cites Revealing the mechanisms of ammonia dual- fuel combustion for decarbonization in marine transportation.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Revealing the mechanisms of ammonia dual- fuel combustion for decarbonization in marine transportation

Reference 13

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This paper cites Numerical simulation of ammonia/methane/air combustion using reduced chem- ical kinetics models.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Numerical simulation of ammonia/methane/air combustion using reduced chem- ical kinetics models

Reference 14

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This paper cites Predictions of NO and CO emissions in ammonia/methane/air combus- tion by LES using a non-adiabatic flamelet generated manifold.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Predictions of NO and CO emissions in ammonia/methane/air combus- tion by LES using a non-adiabatic flamelet generated manifold

Reference 15

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Machine Learning for Chemical Reactions

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This paper cites A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kinetics

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Machine learning for com- bustion chemistry

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Artificial intelligence as a catalyst for combustion science and engineering

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Numerical simulation of tur- bulent combustion: Scientific challenges

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Machine learning tabu- lation of thermochemistry in turbulent combustion: An approach based on hybrid flamelet/random data and multiple multilayer perceptrons

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Criteria to switch from tabulation to neural networks in computational combustion

Reference 22

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation On-the-fly artificial neural network for chemical kinetics in direct numerical simulations of premixed combus- tion

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Chemistry reduction using machine learning trained from non-premixed micro-mixing modeling: Application to DNS of a syngas turbulent oxy-flame with side-wall ef- fects

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Artificial neural network chem- istry solving for high-pressure hydrogen–air combustion

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Unresolved cited work

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Tabulation of combus- tion chemistry via Artificial Neural Networks (ANNs): Methodology and application to LES-PDF simulation of Sydney flame L

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Simulation of turbulent premixed flames with machine learning - tabulated thermochemistry

Reference 29

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation A feasibility study on the use of low-dimensional simulations for database generation in adaptive chem- istry approaches

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Simplifying chemical kinetics: Intrinsic low- dimensional manifolds in composition space

Reference 31

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Graphics process- ing unit/artificial neural network-accelerated large-eddy simulation of swirling premixed flames

Reference 32

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Comprehensive deep learning for combustion chemistry integration: Multi-fuel gener- alization anda posteriorivalidation in reacting flow

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Experimental and numerical study of the laminar burning velocity of CH4–NH3–air premixed flames

Reference 34

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This paper cites Cantera: An Object-oriented Software Toolkit for Chemical Kinetics, Thermody- namics, and Transport Processes.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Cantera: An Object-oriented Software Toolkit for Chemical Kinetics, Thermody- namics, and Transport Processes

Reference 35

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This paper cites DeepFlame: A deep learning empowered open-source platform for reacting flow simulations.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

Reference 36

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation An integrated framework for accelerating reactive flow simulation using GPU and ma- chine learning models

Reference 37

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation DeepFlame 2.0: A new version for fully GPU-native machine learning accelerated reacting flow simulations under low-Mach conditions

Reference 38

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Machine learning tabulation of ther- mochemistry of fuel blends

Reference 39

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Delving into Deep Imbalanced Regression

Reference 40

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This paper cites Data-driven simulation of ammonia combustion using neural ordinary differential equations (NODE).

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Data-driven simulation of ammonia combustion using neural ordinary differential equations (NODE)

Reference 41

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Clustering-Enhanced Deep Learning Method for Computation of Full Detailed Ther- mochemical States via Solver-Based Adaptive Sampling

Reference 42

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Combust Theory and Model

Reference 43

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation An improved approach towards more robust deep learning models for chemical kinetics

Reference 44

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Unresolved cited work

Reference 45

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Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation Efficient machine learningmethodforsupercriticalcombustion: Predictingreal-fluidprop- erties and chemical ODEs

Reference 46

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Observation 20578ee0-15bc-4459-b986-0c0616cf7015 · outbound

This paper cites DNSLab: A gateway to turbulent flow sim- ulation in Matlab.

Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation DNSLab: A gateway to turbulent flow sim- ulation in Matlab

Reference 47

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Pith citing papers

No inbound Pith citation observations are available.