Byrne, S., Wilcox, L.C., Churavy, V., 2021: MPI.jl: Julia bindings for the Message Passing Interface. JuliaCon Proceedings, 1, 68.
[PDF] [Official Version]
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.
[PDF] [Official Version]
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.
[PDF] [Official Version]
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.
[PDF] [Official version]
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.
[PDF] [Official version]
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.
[PDF] [Official Version]
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.
[PDF]
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.
[PDF]
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.
[PDF] [Official Version]
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]