Gallery

Here we provide a gallery containing selected example results of several applications (a non-exhaustive list) using SPUX framework for Bayesian inference and uncertainty quantification.

Randomwalk

Domain: demo.

Authors: Jonas Šukys (Eawag, Switzerland).

Model: one-dimensional random walk (built-in).

Language: Python.

Cluster: EULER (ETH Zurich, Switzerland) - 129 cores.

A simple one-dimensional randomwalk with uncertain origin, drift, and the observation error.

Posterior distribution of model predictions for the observational dataset. The shaded orange regions indicate the log-density of the posterior model predictions distribution at the respective time points, the brown line indicates the approximate MAP model prediction., the red line represents the exact model prediction values.

Joint pairwise marginal posterior distribution of origin and drift, including the corresponding Markov chains from the sampler. Legend: thin semi-transparent gray lines and dots - concurrent chains, orange hexagons - histogram of the joint pairwise marginal posterior parameters samples, blue “+” - initial parameters, brown “o” - approximate MAP parameters, red “x” - the exact parameters.

Linear bucket

Domain: hydrology.

Authors: Andreas Scheidegger (Eawag, Switzerland).

Model: linear bucket model with stochastic forcing.

Language: R, with rpy2 bindings to Python.

Work in progress.

Stochastic inputs

Domain: hydrology.

Authors: Jonas Šukys (Eawag, Switzerland).

Model: hydrological model with stochastic inputs (built-in).

Language: Python, with numba compiled C code for computationally expensive parts.

Cluster: EULER (ETH Zurich, Switzerland) - 1121 cores.

Publication: Del Giudice, D. et al., (2016) “Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation; Water Resources Research. John Wiley & Sons, Ltd, 52(4), pp. 3162–3186. doi: 10.1002/2015WR017871.

The first dataset and the associated heteroscedastic error model for the input (precipitation) and the output (discharge) measurements.

Plots of the posterior distribution of model predictions for an observational dataset above, including auxiliary posterior distributions for rainfall potential $\xi$, reservoir level S, and the water volume discrepancy $\Delta V$.

Work in progress.

Stochastic parameters

Domain: hydrology.

Authors: Marco Bacci, Jonas Šukys (Eawag, Switzerland).

Model: hydrological model with stochastic time-dependent parameters (Superflex).

Language: Fortran, with ctypes bindings of the compiled Fortran model library to Python.

Work in progress.

Prey-Predator

Domain: aquatic ecology.

Authors: Jonas Šukys, Nele Schuwirth, Peter Reichert (Eawag, Switzerland), Mira Kattwinkel (University of Koblenz-Landau, Germany).

Model: prey-predator model using stochastic individual based model with synthetic dataset (IBM-Bugs).

Language: Java, with JPype bindings to Python.

Cluster: EULER (ETH Zurich, Switzerland) - up to 1000 cores.

Publication (preprint available at http://arxiv.org/abs/1711.01410):

Šukys, J. and Kattwinkel, M.
"SPUX: Scalable Particle Markov Chain Monte Carlo
for uncertainty quantification in stochastic ecological models".
Advances in Parallel Computing - Parallel Computing is Everywhere,
IOS Press, (32), pp. 159–168, 2018.

Marginal posterior distribution of prey_kDens and predator_kDens parameters, including the corresponding MCMC chain from the sampler. Legend: green “+” - initial parameters.

Work in progress.

River invertebrates

Domain: aquatic ecology.

Authors: Marco Bacci, Nele Schuwirth, Peter Reichert, Jonas Šukys (Eawag, Switzerland) Mira Kattwinkel (U Koblenz-Landau, Germany).

Model: river invertebrates mesocosm modeling using stochastic IBMs (IBM-Bugs).

Model: Java, with JPype bindings to Python.

Cluster: EULER (ETH Zurich, Switzerland) - 736 cores.

Work in progress.

DATALAKES

Domain: hydrology and data science.

Authors: Artur Safin, Jonas Šukys (Eawag, Switzerland).

Model: DATALAKES - a scalable UQ framework for predicting lake dynamics (MITgcm).

Language: Fortran, with ctypes bindings of the compiled Fortran model library to Python.

Cluster: Daint (Swiss Supercomputing Center (CSCS), Switzerland).

Work in progress.

In-stream herbicides

Domain: aquatic ecology.

Authors: Peter Reichert, Fabrizio Fenizia, Lorenz Ammann, Jonas Šukys (Eawag, Switzerland).

Model: in-stream herbicide concentration dynamics (Superflex).

Language: Fortran, with ctypes bindings of the compiled Fortran model library to Python.

Work in progress.

Urban hydrology

Domain: urban hydrology.

Authors: Joao Leitao, Andreas Scheidegger, Jörg Rieckermann, Jonas Šukys.

Model: urban hydrologic model (SWMM).

Language: C, with Swig wrapper for Python.

Work in progress.

Solar dynamo

Domain: physics and data science

BISTOM - calibration of the solar dynamo simulations.

Work in progress.