By insulating the inverse algorithm against ill-posed mathematical growth, Tikhonov regularization flattens a staggering 25% sensor distortion down to less than 0.003% filtered noise power.
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Stabilizing the Helmholtz Cauchy Problem in Active Sonar Pipelines
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graph TD
%% Node Definitions - Current Exploration
subgraph Current_Exploration [Current Analytical Pillars]
Eq[("Governing Equation: Inhomogeneous Helmholtz")]
Model["Forward/Inverse Model: Cylindrical Bessel/Hankel Functions"]
Noise["Ill-Posedness: Exponential Noise Explosion as $$\ r \to 0$$"]
Reg["Numerical Stabilization: Tikhonov Regularization"]
end
%% Node Definitions - Progression Path
Start(Start: Stabilized 2D Cauchy Problem) --> Current_Exploration
Current_Exploration --> Analyze{"Analyze System Performance"}
Analyze -- "Static Penalty Insufficient" --> AutoReg
%% Node Definitions - Potential Exploration Vectors
subgraph Future_Vectors [Potential Exploration Vectors]
direction TB
subgraph Geo_Ext [Geometric Extensions]
Dir3D[Transition to 3D Spherical Geometries]
SphBasis["Utilize Spherical Bessel (jₙ) & Hankel (hₙ¹) Functions"]
end
subgraph Math_Adv [Mathematical Advancements]
AutoReg[Implement Dynamic Regularization L-Curve / GCV]
NonLin["Integrate Iterative Inversion (DBIM / FWI) for Nonlinear Scattering"]
end
subgraph Viz_Adv [Visualization Advancements]
ManimExp["Extend Manim Animations for Spectral Penalty Visualization"]
end
end
%% Connections
Dir3D --> SphBasis
Analyze -- "Standard 2D Limit Reached" --> Dir3D
AutoReg -.-> Analyze
Analyze -- "Linearization Artifacts Present" --> NonLin
Analyze -- "Need Deeper Insight into Filtering" --> ManimExp
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classDef viz fill:#451a03,stroke:#f59e0b,stroke-width:2px,color:#fffbeb;
class Start startNode;
class Eq,Model,Noise,Reg current;
class Analyze choice;
class Dir3D,SphBasis geo;
class AutoReg,NonLin math;
class ManimExp viz;
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https://hackmd.io/@bafMjVSBQ6e7vlO7NdkhXA/rJ6zMkpdMx
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