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Determining a parabolic-elliptic-elliptic system by boundary observation of its non-negative solutions under chemotaxis background

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abstract

This paper addresses a profoundly challenging inverse problem that has remained largely unexplored due to its mathematical complexity: the unique identification of all unknown coefficients in a coupled nonlinear system of mixed parabolic-elliptic-elliptic type using only boundary measurements. The system models attraction-repulsion chemotaxis--an advanced mathematical biology framework for studying sophisticated cellular processes--yet despite its significant practical importance, the corresponding inverse problem has never been investigated, representing a true frontier in the field. The mixed-type nature of this system introduces significant theoretical difficulties that render conventional methodologies inadequate, demanding fundamental extensions beyond existing techniques developed for simpler, purely parabolic models. Technically, the problem presents formidable obstacles: the coupling between parabolic and elliptic components creates inherent analytical complications, while the nonlinear structure resists standard approaches. From an applied perspective, the biological relevance adds another layer of complexity, as solutions must maintain physical interpretability through non-negativity constraints. Our work provides a complete theoretical framework for this challenging problem, establishing rigorous unique identifiability results that create a one-to-one correspondence between boundary data and the model's parameters. We demonstrate the power of our general theory through a central biological application: the full parameter recovery for an attraction-repulsion chemotaxis model with logistic growth, thus opening new avenues for quantitative analysis in mathematical biology.

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representative citing papers

Unveiling Biological Models Through Turing Patterns

math.AP · 2025-09-09 · reject · novelty 5.0

The paper proposes recovering all diffusion and chemotaxis parameters from the Fourier amplitudes of a single Turing pattern, but the uniqueness proof is incomplete and unvalidated.

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  • Unveiling Biological Models Through Turing Patterns math.AP · 2025-09-09 · reject · none · ref 16 · internal anchor

    The paper proposes recovering all diffusion and chemotaxis parameters from the Fourier amplitudes of a single Turing pattern, but the uniqueness proof is incomplete and unvalidated.