Byrne, S., Wilcox, L.C., Churavy, V., 2021: **MPI.jl: Julia bindings for the Message Passing Interface**. *JuliaCon Proceedings*, **1**, 68.

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# All Posts

# Reconciling Bayesian and perimeter regularization for binary inversion

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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# Ensemble inference methods for models with noisy and expensive likelihoods

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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# Ensemble-based experimental design for targeted high-resolution simulations to inform climate models

Dunbar, O.R.A., Howland, M.F., Schneider, T., Stuart, A.M., 2022: **Ensemble-based experimental design for targeting data requisition to inform climate models**. *Journal of Advances in Modeling Earth Systems*, **14**, e2022MS002997.

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# An efficient Bayesian approach to learning droplet collision kernels: Proof of concept using “Cloudy”, a new n-moment bulk microphysics scheme

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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# Iterated Kalman methodology for inverse problems

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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# The cost-accuracy trade-off in operator learning with neural networks

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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# Efficient derivative-free Bayesian inference for large-scale inverse problems

Huang, D.Z., Huang, J., Reich, S., Stuart, A.M., 2022: **Efficient derivative-free Bayesian inference for large-scale inverse problems**. *Inverse Problems*, submitted.

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# PySDM v1: particle-based cloud modeling package for warm-rain microphysics and aqueous chemistry

Bartman, P., Bulenok, O., Górski, K., Jaruga, A., Łazarski, G., Olesik, M.A., Piasecki, B., Singer, C.E., Talar, A., Arabas, S., 2022: **PySDM v1: particle-based cloud modeling package for warm-rain microphysics and aqueous chemistry**. *Journal of Open Source Software,* **7**, 3219.

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# Spanning the gap from bulk to bin: a novel spectral microphysics method

de Jong, E., Bischoff, T., Nadim, A., Schneider, T., 2022: **Spanning the gap from bulk to bin: a novel spectral microphysics method**. *Journal of Advances in Modeling Earth Systems*, **14**, e2022MS003186.

[PDF] [Official Version]