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The inverse problem for neural networks

• Authors : Forets, Marcelo; Schilling, Christian; Steffen, Bernhard

Subjects: Neural network; Inverse problem; Set propagation

• Source: Forets, M & Schilling, C 2024, The inverse problem for neural networks. in B Steffen (ed.), Bridging the Gap Between AI and Reality :

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DeepQSM:Solving the quantitative susceptibility mapping inverse problem using deep learning

• Authors : Kristensen, Mads Janus; Rasmussen, Kasper Gade Bøtker; Blendal, Rasmus Guldhammer

• Source: Kristensen, M J, Rasmussen, K G B, Blendal, R G, Østergaard, L R, Plocharski, M, Janke, A, Langkammer, C, O'Brien, K, Barth, M & Bollmann, S 2018, DeepQSM : Solving the quantitative susceptibility

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A New Method to Determine Thermal Properties of the Mixture of PCM and Concrete

• Authors : R., Cheng; Pomianowski, Michal Zbigniew; Heiselberg, Per

Subjects: Phase change material; Thermal physical properties; Inverse problem

• Source: R., C, Pomianowski, M Z, Heiselberg, P, Wang, X & Zang, Y 2012, 'A New Method to Determine Thermal Properties of the Mixture of PCM and Concrete', Paper presented at The 4th International Conference

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Identification of constitutive parameters for thin-walled aluminium tubes using a hybrid strategy

• Authors : Zhang, Bin; Endelt, Benny; Lang, Lihui

Subjects: Finite element method; Hydraulic bulge test; Numerical optimization

• Source: Zhang, B, Endelt, B, Lang, L & Nielsen, K B 2021, 'Identification of constitutive parameters for thin-walled aluminium tubes using a hybrid strategy', Materials Today Communications, vol. 28, 102670.

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Reconstruction of piecewise constant layered conductivities in electrical impedance tomography

• Authors : Garde, Henrik

Subjects: Electrical impedance tomography; monotonicity principle; partial data reconstruction

• Source: Garde, H 2020, 'Reconstruction of piecewise constant layered conductivities in electrical impedance tomography', Communications in Partial Differential Equations, vol. 45, no. 9, pp. 1118-1133.

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Optimal depth-dependent distinguishability bounds for electrical impedance tomography in arbitrary dimension

• Authors : Garde, Henrik; Hyvönen, Nuutti

Subjects: electrical impedance tomography; Kelvin transformation; depth dependence

• Source: Garde, H & Hyvönen, N 2020, 'Optimal depth-dependent distinguishability bounds for electrical impedance tomography in arbitrary dimension', SIAM Journal on Applied Mathematics, vol. 80, no. 1, pp.

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Decompounding random sums: A nonparametric approach

• Authors : Hansen, Martin Bøgsted; Pitts, Susan M.

• Source: Hansen, M B & Pitts, S M 2006, Decompounding random sums: A nonparametric approach. Research Report Series, no. R-2006-20, Department of Mathematical Sciences, Aalborg University.

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