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Dunbar, O.R.A., Dunlop, M.M., Elliott, C.M., Hoang, V.H., Stuart, A.M., 2021: Reconciling Bayesian and perimeter regularization for binary inversion. SIAM Journal on Scientific Computing, 42, A1984−A2013.
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Dunbar, O.R.A, Duncan, A.B., Stuart, A.M., Wolfram, M.-T., 2022: Ensemble inference methods for models with noisy and expensive likelihoods. SIAM Journal on Applied Dynamical Systems, 21, 1539-1572.
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Bieli, M., Dunbar, O.R.A., de Jong, E.K., Jaruga, A., Schneider, T., Bischoff, T., 2022: An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using “Cloudy”, a new n-moment bulk microphysics scheme. Journal of Advances in Modeling Earth Systems, 14, e2022MS002994.
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Huang, D.Z., Schneider, T., Stuart, A.M., 2022: Iterated Kalman methodology for inverse problems. Journal of Computational Physics, 463, 111262.
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de Hoop, M.V., Huang, D.Z., Qian, E., Stuart, A.M., 2022: The cost-accuracy trade-off in operator learning with neural networks. arXiv pre-prints arXiv:2203.13181, submitted.
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