Thermal tomography with unknown boundary
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Thermal tomography with unknown boundary. / Hyvönen, Nuutti; Mustonen, Lauri.
julkaisussa: SIAM Journal on Scientific Computing, Vuosikerta 40, Nro 3, 01.01.2018, s. B662-B683.Tutkimustuotos: Lehtiartikkeli › › vertaisarvioitu
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TY - JOUR
T1 - Thermal tomography with unknown boundary
AU - Hyvönen, Nuutti
AU - Mustonen, Lauri
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Thermal tomography is an imaging technique for deducing information about the internal structure of a physical body from temperature measurements on its boundary. This work considers time-dependent thermal tomography modeled by a parabolic initial/boundary value problem without accurate information on the exterior shape of the examined object. The adaptive sparse pseudospectral approximation method is used to form a polynomial surrogate for the dependence of the temperature measurements on the thermal conductivity, the heat capacity, the boundary heat transfer coefficient, and the body shape. These quantities can then be efficiently reconstructed via nonlinear, regularized least squares minimization employing the surrogate and its derivatives. The functionality of the resulting reconstruction algorithm is demonstrated by numerical experiments based on simulated data in two spatial dimensions.
AB - Thermal tomography is an imaging technique for deducing information about the internal structure of a physical body from temperature measurements on its boundary. This work considers time-dependent thermal tomography modeled by a parabolic initial/boundary value problem without accurate information on the exterior shape of the examined object. The adaptive sparse pseudospectral approximation method is used to form a polynomial surrogate for the dependence of the temperature measurements on the thermal conductivity, the heat capacity, the boundary heat transfer coefficient, and the body shape. These quantities can then be efficiently reconstructed via nonlinear, regularized least squares minimization employing the surrogate and its derivatives. The functionality of the resulting reconstruction algorithm is demonstrated by numerical experiments based on simulated data in two spatial dimensions.
KW - Inaccurate measurement model
KW - Inverse boundary value problems
KW - Sparse pseudospectral approximation
KW - Thermal tomography
UR - http://www.scopus.com/inward/record.url?scp=85049486939&partnerID=8YFLogxK
U2 - 10.1137/16M1104573
DO - 10.1137/16M1104573
M3 - Article
VL - 40
SP - B662-B683
JO - SIAM Journal on Scientific Computing
JF - SIAM Journal on Scientific Computing
SN - 1064-8275
IS - 3
ER -
ID: 26622382