has been cited by the following article(s):
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[1]
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Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Integral Equations of the Volterra Type: Mathematical Methodology and Illustrative Application to Nuclear Engineering
Journal of Nuclear Engineering,
2025
DOI:10.3390/jne6020008
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[2]
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The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Neural Integro-Differential Equations of Volterra Type: Mathematical Framework and Illustrative Application to a Nonlinear Heat Conduction Model
Journal of Nuclear Engineering,
2025
DOI:10.3390/jne6030024
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[3]
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The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Fredholm-Type Neural Integro-Differential Equations: I. Mathematical Framework
Processes,
2025
DOI:10.3390/pr13072258
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[4]
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The First- and Second-Order Features Adjoint Sensitivity Analysis Methodologies for Fredholm-Type Neural Integro-Differential Equations: An Illustrative Application to a Heat Transfer Model—Part II
Processes,
2025
DOI:10.3390/pr13072265
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[5]
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The nth-order features adjoint sensitivity analysis methodology for response-coupled forward/adjoint linear systems (nth-FASAM-L): I. mathematical framework
Frontiers in Energy Research,
2024
DOI:10.3389/fenrg.2024.1417594
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[6]
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First-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations: Mathematical Framework and Illustrative Application to the Nordheim–Fuchs Reactor Safety Model
Journal of Nuclear Engineering,
2024
DOI:10.3390/jne5030023
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[7]
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nth-order feature adjoint sensitivity analysis methodology for response-coupled forward/adjoint linear systems: II. Illustrative application to a paradigm energy system
Frontiers in Energy Research,
2024
DOI:10.3389/fenrg.2024.1421519
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[8]
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Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Neural Ordinary Differential Equations—I: Mathematical Framework
Processes,
2024
DOI:10.3390/pr12122660
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[9]
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Introducing the Second-Order Features Adjoint Sensitivity Analysis Methodology for Fredholm-Type Neural Integral Equations
Mathematics,
2024
DOI:10.3390/math13010014
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