Multioutput Fits

These scripts fit surrogate models to multioutput data. Each takes an input/output dataset, splits it into training and testing subsets, fits the surrogate, and produces parity plots, per-sample overlays, and (where applicable) sensitivity bar charts.

kl_fit.py

Build Karhunen–Loève (KL) decompositions of multioutput data.

This script reads a multioutput dataset, performs a KL expansion to reduce its dimensionality, and generates diagnostic plots including explained-variance curves, data-vs-approximation scatter plots, and sample-wise fit comparisons.

Outputs:

• dm_*.png — Diagonal model-vs-approximation scatter plots per output.

• fit_s*.png — Per-sample overlay of original and KL-reconstructed outputs.

Example:

python kl_fit.py -y ydata.txt -e 5

Arguments:

Flag	Default	Description
-d, --xcond		Conditioning x-grid file (default: index array).
-y, --ydata	ydata.txt	Multioutput data file.
-e, --neig		Number of eigenvalues to retain (default: auto at 99%).

klsurr_fit.py

Build KL-based reduced-dimensional surrogates of multioutput models.

This script combines a Karhunen–Loève expansion with surrogate modelling (Polynomial Chaos or Neural Network) to construct a reduced-dimensional surrogate for multioutput data. It produces parity plots, per-sample fit comparisons, and global sensitivity bar charts.

Outputs:

• dm_*.png — Parity plots per output.

• fit_s*.png — Per-sample overlay of model and KL+surrogate predictions.

• sens_klsurr.png — Global sensitivity bar chart.

• klsurr.pk — Pickled KLSurr object.

Example:

python klsurr_fit.py -x ptrain.txt -y ytrain.txt -s PC -m bcs -o 3

Arguments:

Flag	Default	Description
-x, --xdata	ptrain.txt	Input data file.
-y, --ydata	ytrain.txt	Output data file.
-d, --xcond		Conditioning x-grid file.
-q, --outnames_file	outnames.txt	Output names file.
-p, --pnames_file	pnames.txt	Parameter names file.
-t, --trnfactor	0.9	Fraction of data used for training.
-s, --surr	PC	Surrogate type: PC or NN.
-m, --method	bcs	Fitting method: lsq, bcs, or anl.
-c, --pctype	LU	PC basis type: LU or HG.
-o, --order	1	Polynomial chaos order.

nn_fit.py

Build neural-network-based surrogates for multioutput models.

This script trains a Residual Network (RNet from QUiNN) on user-supplied input/output data, splits the data into training and testing sets, and produces parity plots and per-sample fit comparisons.

Requires the QUiNN package.

Outputs:

• dm_*.png — Parity (model vs approximation) plots per output.

• fit_s*.png — Per-sample overlay of original and NN-predicted outputs.

Example:

python nn_fit.py -x ptrain.txt -y ytrain.txt -t 0.8

Arguments:

Flag	Default	Description
-x, --xdata	ptrain.txt	Input data file.
-y, --ydata	ytrain.txt	Output data file.
-d, --xcond		Conditioning x-grid file.
-q, --outnames_file	outnames.txt	Output names file.
-p, --pnames_file	pnames.txt	Parameter names file.
-t, --trnfactor	0.9	Fraction of data used for training.