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Inverse problems for time-dependent nonlinear transport equations

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arxiv 2410.00369 v1 pith:CV6LID4Z submitted 2024-10-01 math.AP

classification math.AP
keywords inversenonlineartransportcoefficientequationinitialproblemtime-dependent
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abstract

In this work, we investigate inverse problems of recovering the time-dependent coefficient in the nonlinear transport equation in both cases: two-dimensional Riemannian manifolds and Euclidean space $\mathbb{R}^n$, $n\geq 2$. Specifically, it is shown that its initial boundary value problem is well-posed for small initial and incoming data. Moreover, the time-dependent coefficient appearing in the nonlinear term can be uniquely determined from boundary measurements as well as initial and final data. To achieve this, the central techniques we utilize include the linearization technique and the construction of special geometrical optics solutions for the linear transport equation. This allows us to reduce the inverse coefficient problem to the inversion of certain weighted light ray transforms. Based on the developed methodology, the inverse source problem for the nonlinear transport equation in the scattering-free media is also studied.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The BGK model with in-flow boundary condition: forward and inverse problems

    math.AP 2026-07 accept novelty 6.0 of 10

    Local weighted-L∞ well-posedness for the BGK model with inflow BC near equilibrium, and unique recovery of γ(x)ρ^α T^β from the albedo map via linearization and concentrated test data.

  2. Carleman--Picard and time-dimensional reduction for inverse initial-data problems in nonlinear transport with memory

    math.NA 2026-08 conditional novelty 5.0 of 10

    A Carleman-Picard iteration with Legendre-exponential time reduction globally converges, within a truncated reduced model, for reconstructing initial data of quasilinear transport with memory from outflow measurements.

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