Peer-reviewed veterinary case report
How to identify time-dependent coefficients in equations?
By A J Al-Shatrah M & Sabah Hussein M.·2026·Ministry of Education·View original on Europe PMC →
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Original publication title: Simultaneous Numerical Determination of Two Time-dependent Coefficients in Second Order Parabolic Equation With Nonlocal Initial and Boundary Conditions.
Plain-English summary
This study focuses on a mathematical method for identifying two changing factors in a specific type of equation used in heat transfer and diffusion processes. The researchers used a reliable computational technique to solve the problem and reformulated it to make it easier to find the unknown factors. They found that using a regularization method helped improve the accuracy of their results, even when the data was not perfect. Overall, the approach they developed is effective and efficient for applications in related scientific fields.
Abstract
<h4>Background</h4>This study establishes a mathematically consistent and computational framework for the simultaneous identification of two time-dependent coefficients in a one-dimensional second-order parabolic partial differential equation. The considered problem is governed by nonlocal initial, boundary, and integral overdetermination conditions.<h4>Methods</h4>The direct problem is solved using the Crank-Nicolson finite difference method (FDM), which ensures unconditional stability and second-order accuracy in both spatial and temporal discretizations. The corresponding inverse problem is reformulated as a nonlinear regularized least-squares optimization problem and efficiently solved using the MATLAB subroutine <i>lsqnonlin</i> from the optimization Toolbox. Due to the intrinsic ill-posedness of the inverse formulation, small input data errors lead to big output errors. Then, Tikhonov regularization is employed to enhance numerical stability and robustness.<h4>Results</h4>Extensive numerical experiments are carried out under exact and noisy data to evaluate the numerical accuracy and convergence behavior of the method. The results confirm that the regularization technique effectively damps numerical oscillations, minimizes reconstruction error, and ensures reliable recovery of the unknown coefficients. Sensitivity analysis further reveals the essential role of the regularization parameter in controlling the trade-off between stability and accuracy.<h4>Conclusions</h4>The proposed approach provides an accurate and computationally efficient tool for IP in heat transfer, diffusion processes, and related applied sciences.
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Search related cases →Original publication on Europe PMC: https://europepmc.org/article/MED/41800216