Main Functionality
- A Redhat enterprise Linux 7.9 is available for ODYSSEE Solver 2023.1.
ODYSSEE Quasar – Solver
- A Quasar-Python wrapper has been developed to use the Quasar’s external functions in Python code. For details on “How to use it?”, please refer to Python Wrapper chapter in the Quasar help.
- Solver:
- A new solver is available in Quasar, the SVM (Support Vector Machine) option classification dedicated to classification problems using hyperplanes to separate data.
SVM("classification", X, Y, XN, kernel_type, C, gamma, degree, save_files, prefix)
- Backward compatibility to run POD binaries between 2022.3 and 2023.1.
- Quasar Help document:
- An LSTM example has been added.
- SVM example has been added.
- Two Useful function have been added:
- cumtrapz (cumulative trapezoidal integration) function computes an approximation of the cumulative integral of Y via the trapezoidal method with unit spacing.
Matrix cumtrapz(Matrix Mat, Matrix IntegrationAxis, int orientation)
- rolling_ave function calculates the rolling average on a full curve. The mean is based on the previous points located a Window where the size is given.
Matrix rolling_ave(Matrix Mat,int windowSize,int orientation
- FMU Improvements:
- At the beginning of the xml file, a xml tag <fmiModelDescription> is added containing the version used of ODYSSEE CAE for the generation, the creation date and the current version of the FMU.
- Updated xml <ModelStructure> at the end of the xml file by adding <InitialUnknown…> information, thus fixing some execution errors in ADAMS.
- Improved performance for prediction in FMU ROM Smart Superelement (pod_krg mainly). It improves performance for large training datasets.
- Some fixes have been done on the qsr file inside the FMU for the loading data in memory.
- The FMU generated in Windows OS are compatible with an execution with ODYSSEE Solver Linux Redhat 7.9.
- Bug Fixes
- For the prediction of the animations with the option “elemental data” selected, when POD was selected with “recompute database file” unselected (save_file=-2) the calculation took the same time as the option “recompute database file” (save_files= -1).
- For the curves prediction, with mode number different of 0 (0=all modes), the result was incorrect.
- For the INVD predictive method, if the new point to be predicted (Xn) was already known in the X-learning database (Xn=X), then the result Yn obtained was from the predictive formula. We changed it to give a result equal to the known value in the Y-learning database.
- The Tx argument in LSTM function (length of one sequence of data = number of points describing one repetitive cycle) didn’t work.
- The function order_y_by_row_index_x has been fixed. It correctly generates a sorted Y matrix depending on a column of X matrix.
- With the method POD, a bad interpolation issue appeared when a reduced number of modes was used with “do training step” at “no” (save_file = -2).
ODYSSEE Nova – Optimizer
- Bug Fixes
- The final optimal point displayed in the summary through the command prompt was not the good one selected among the convergence points when we worked with multiple objective functions Fi. The log file has been updated to correctly display the optimal point, with constraints if there are.
- For an optimization problem with an equality constraint, the optimal point proposed at the end of the log file was not appropriate if the problem had 2 or more parameters.
- For the methods downhill, simulated, SQP, SLSQP and NLPQLP, after the convergence, the result F (evaluation function) was missing in the summary of the optimal point.