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Second-order phase transition in phytoplankton trait dynamics
Held, J., Lorimer, T., Pomati, F., Stoop, R., & Albert, C. (2020). Second-order phase transition in phytoplankton trait dynamics. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(5), 053109 (9 pp.). https://doi.org/10.1063/1.5141755
Data assimilation and online parameter optimization in groundwater modeling using nested particle filters
Ramgraber, M., Albert, C., & Schirmer, M. (2019). Data assimilation and online parameter optimization in groundwater modeling using nested particle filters. Water Resources Research, 55, 9724-9747. https://doi.org/10.1029/2018WR024408
Signature-domain calibration of hydrological models using Approximate Bayesian Computation: empirical analysis of fundamental properties
Fenicia, F., Kavetski, D., Reichert, P., & Albert, C. (2018). Signature-domain calibration of hydrological models using Approximate Bayesian Computation: empirical analysis of fundamental properties. Water Resources Research, 54(6), 3958-3987. https://doi.org/10.1002/2017WR021616
Signature-domain calibration of hydrological models using Approximate Bayesian Computation: theory and comparison to existing applications
Kavetski, D., Fenicia, F., Reichert, P., & Albert, C. (2018). Signature-domain calibration of hydrological models using Approximate Bayesian Computation: theory and comparison to existing applications. Water Resources Research, 54(6), 4059-4083. https://doi.org/10.1002/2017WR020528
Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator
Machac, D., Reichert, P., Rieckermann, J., Del Giudice, D., & Albert, C. (2018). Accelerating Bayesian inference in hydrological modeling with a mechanistic emulator. Environmental Modelling and Software, 109, 66-79. https://doi.org/10.1016/j.envsoft.2018.07.016
Appraisal of data-driven and mechanistic emulators of nonlinear simulators: the case of hydrodynamic urban drainage models
Carbajal, J. P., Leitão, J. P., Albert, C., & Rieckermann, J. (2017). Appraisal of data-driven and mechanistic emulators of nonlinear simulators: the case of hydrodynamic urban drainage models. Environmental Modelling and Software, 92, 17-27. https://doi.org/10.1016/j.envsoft.2017.02.006
Hebbian learning clustering with Rulkov neurons
Held, J., Lorimer, T., Albert, C., & Stoop, R. (2017). Hebbian learning clustering with Rulkov neurons. In G. Mantica, R. Stoop, & S. Stramaglia (Eds.), Springer proceedings in physics: Vol. 191. Emergent complexity from nonlinearity, in physics, engineering and the life sciences (pp. 127-141). https://doi.org/10.1007/978-3-319-47810-4_11
Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation
Albert, C., Ulzega, S., & Stoop, R. (2016). Boosting Bayesian parameter inference of nonlinear stochastic differential equation models by Hamiltonian scale separation. Physical Review E, 93(4), 1-8. https://doi.org/10.1103/PhysRevE.93.043313
Computationally efficient implementation of a novel algorithm for the General Unified Threshold model of Survival (GUTS)
Albert, C., Vogel, S., & Ashauer, R. (2016). Computationally efficient implementation of a novel algorithm for the General Unified Threshold model of Survival (GUTS). PLoS Computational Biology, 12(6), 1-19. https://doi.org/10.1371/journal.pcbi.1004978
Modelling survival: exposure pattern, species sensitivity and uncertainty
Ashauer, R., Albert, C., Augustine, S., Cedergreen, N., Charles, S., Ducrot, V., … Preuss, T. G. (2016). Modelling survival: exposure pattern, species sensitivity and uncertainty. Scientific Reports, 6, 29178 (11 pp.). https://doi.org/10.1038/srep29178
Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation
Del Giudice, D., Albert, C., Rieckermann, J., & Reichert, P. (2016). Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation. Water Resources Research, 52(4), 3162-3186. https://doi.org/10.1002/2015WR017871
Emulation of dynamic simulators with application to hydrology
Machac, D., Reichert, P., & Albert, C. (2016). Emulation of dynamic simulators with application to hydrology. Journal of Computational Physics, 313(May), 352-366. https://doi.org/10.1016/j.jcp.2016.02.046
Fast mechanism-based emulator of a slow urban hydrodynamic drainage simulator
Machac, D., Reichert, P., Rieckermann, J., & Albert, C. (2016). Fast mechanism-based emulator of a slow urban hydrodynamic drainage simulator. Environmental Modelling and Software, 78, 54-67. https://doi.org/10.1016/j.envsoft.2015.12.007
Big data naturally rescaled
Stoop, R., Kanders, K., Lorimer, T., Held, J., & Albert, C. (2016). Big data naturally rescaled. Chaos, Solitons & Fractals, 90, 81-90. https://doi.org/10.1016/j.chaos.2016.02.035
A simulated annealing approach to approximate Bayes computations
Albert, C., Künsch, H. R., & Scheidegger, A. (2015). A simulated annealing approach to approximate Bayes computations. Statistics and Computing, 25(6), 1217-1232. https://doi.org/10.1007/s11222-014-9507-8
Improving output and input statistical error descriptions in urban hydrological modeling
Del Giudice, D. (2015). Improving output and input statistical error descriptions in urban hydrological modeling [Doctoral dissertation, ETH Zürich]. https://doi.org/10.3929/ethz-a-010536371
Model bias and complexity - understanding the effects of structural deficits and input errors on runoff predictions
Del Giudice, D., Reichert, P., Bareš, V., Albert, C., & Rieckermann, J. (2015). Model bias and complexity - understanding the effects of structural deficits and input errors on runoff predictions. Environmental Modelling and Software, 64, 205-214. https://doi.org/10.1016/j.envsoft.2014.11.006
Response to: "Critical analysis of a hypothesis of the planetary tidal influence on solar activity" by S. Poluianov and I. Usoskin
Abreu, J. A., Albert, C., Beer, J., Ferriz-Mas, A., McCracken, K. G., & Steinhilber, F. (2014). Response to: "Critical analysis of a hypothesis of the planetary tidal influence on solar activity" by S. Poluianov and I. Usoskin. Solar Physics, 289(6), 2343-2344. https://doi.org/10.1007/s11207-014-0473-2
The challenge of clustering flow cytometry data from phytoplankton in lakes
Glüge, S., Pomati, F., Albert, C., Kauf, P., & Ott, T. (2014). The challenge of clustering flow cytometry data from phytoplankton in lakes. In V. M. Mladenov & P. C. Ivanov (Eds.), Communications in Computer and Information Science: Vol. 438. Nonlinear Dynamics of Electronic Systems, Communications in Computer and Information Science. 22nd International Conference, NDES 2014, Albena, Bulgaria,July 4-6, 2014. Proceedings (pp. 379-386).
The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction
Rinderknecht, S. L., Albert, C., Borsuk, M. E., Schuwirth, N., Künsch, H. R., & Reichert, P. (2014). The effect of ambiguous prior knowledge on Bayesian model parameter inference and prediction. Environmental Modelling and Software, 62, 300-315. https://doi.org/10.1016/j.envsoft.2014.08.020