We investigate the inverse problem of numerically identifying unknown initial temperatures in a heat equation with dynamic boundary conditions whenever some overdetermination data is provided after a final time. This is a backward parabolic problem which is severely ill-posed. As a first step, the problem is reformulated as an optimization problem with an associated cost functional. Using the weak solution approach, an explicit formula for the Fr\'echet gradient of the cost functional is derived from the corresponding sensitivity and adjoint problems. Then the Lipschitz continuity of the gradient is proved. Next, further spectral properties of the input-output operator are established. Finally, the numerical results for noisy measured data are performed using the regularization framework and the conjugate gradient method. We consider both one- and two-dimensional numerical experiments using finite difference discretization to illustrate the efficiency of the designed algorithm. Aside from dealing with a time derivative on the boundary, the presence of a boundary diffusion makes the analysis more complicated. This issue is handled in the 2-D case by considering the polar coordinate system. The presented method implies fast numerical results.
翻译:我们调查了在热方程式中以动态边界条件从数字上识别未知初始温度的反面问题,如果在最后一段时间后提供某些超度数据,则会用动态边界条件,从数字上识别未知初始温度。这是一个后向抛物线问题,这是一个严重错误。作为第一步,这一问题被重新确定为相关成本功能的优化问题。使用薄弱的解决方案方法,成本函数Fr\'echet梯度的明确公式来自相应的敏感度和连带问题。随后证明了梯度的利普施茨连续性。接着,确定了输入输出操作器的更深频谱特性。最后,使用正规化框架和同位梯度方法对测数据进行了数字测量结果。我们考虑采用一维和二维的数值实验,使用一定的离差来说明设计算法的效率。除了处理边界上的时间衍生物外,边界扩散使分析更加复杂。这个问题通过考虑极地坐标系统处理。提出的方法意味着快速的数字结果。