A Bayesian inference approach to the inverse heat conduction problem

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Stochastic Collocation Algorithms Using l1-Minimization for Bayesian Solution of Inverse Problems (2015, methodology): “This is then used for posterior evaluation or sampling [1, 16, 17, 20, 23, 30, 39].”
A markov random field model of contamination source identification in porous media flow (2006, methodology): “Very recently, the model was introduced to inverse heat transfer problems by Wang and Zabaras [10, 11], where time is treated as an extra spatial dimension in modeling dynamic fields.”
Stochastic spectral methods for efficient Bayesian solution of inverse problems (2005, background): “Diffusive source inversion problems themselves arise in the context of porous media flows [55] and heat conduction [56] [57] [58] [59] [60] We begin by defining the deterministic forward problem G(m), since this is the basis for the general Bayesian approach to inverse problems described in Section…”