DassFlow is a code simulating river dynamics either based on the 1D Saint-Venant system (1D SW), DassFlow1D code, or on the 2D Shallow Water system (2D SW). DassFlow 2D code enables 2D zooms on 1D-like networks with a single solver. SMASH is a code simulating hydrology (spatially distributed model) at watershed scale. DassHydro is a code integrating SMASH and DassFlow, which are weakly coupled in the way SMASH - DassFlow 2D.
The three codes (
DassFlow2D
,
DassFlow1D
and
SMASH
) have been built up following the same spirit and technologies.
The computational kernel codes are written in Fortran 90 with parallel capabilities for DassFlow2D and SMASH (useless feature for DassFlow1D).
The Fortran kernel codes are wrapped in Python using f90wrap [Kermode, 2020].
This enables to use numerous Python libraries on top of the physics-based computations such as pre or post-processing of the computed fields or data, but also Machine Learning, wavelet basis etc.
UQ libraries such as OpenTurns can be easily jointly employed too.
Linear systems are solved using MUMPS or NumPy libraries.
Each code version includes a forward code, the corresponding adjoint code generated by performing the automatic differentiation tool Tapenade [Hascoet and Pascual, 2013].
Each code version includes various optimization algorithms and estimation approaches (e.g. Bayesian analysis).
The typical optimization algorithms are (non exhaustive list):
The complete optimization processes provide parameters identification, reduced uncertainties, calibrated model(s).
VDA formulations are based on priors: optimization starting points and covariances operators (probabilistic priors).
Classical and original covariances operators can be considered.
Several regularization strategies can be considered.
Hybrid physics based modeling with Machine Learning techniques.
Differentiable codes interfaced in Python can be easily combined with external Machine Learning librairies and algorithms (data flux in forward mode, gradient composition for inverse mode).
Reduced order 2D flows model based hybrid POD-NN (Proper Orthogonal Decomposition - Neural Networks) enabling extremly fast simulations.
Each code is interfaced with few pre and post-processors enabling to tackle large scale real-world datasets: GIS libraries, mesh generators, visualization tools.
Keywords: Python-wrapped computational codes; Fortran computational kernels (utilizing MPI for parallelization); adjoint codes; optimization libraries; pre and post-processing Python interfaces; mesh procedures for models coupling.