Dakota vs modeFRONTIER

Dakota provides software for optimization and uncertainty quantification using computational models. Its methods cover optimization, uncertainty quantification, parameter estimation, and sensitivity or variance analysis. These capabilities can be combined into strategies such as hybrid optimization, surrogate-based optimization and optimization under uncertainty. The C++ toolkit connects simulation codes with iterative systems-analysis methods, and Dakota lists MPI for parallel distributed-memory communication. The project describes engineering analysts using it to explore designs and assess confidence in computational predictions. Prebuilt binaries are available for macOS, Windows and Red Hat Enterprise Linux 7 and 8; other platforms require building from source. Installation instructions also describe an optional PYTHONPATH setup for the dakota.interfacing Python module. Dakota versions 5.0 and newer are available under the GNU Lesser General Public License. Some optional numerical libraries, including DOT, NLPQLP and NPSOL/NLSSOL, require separate commercial licenses. Documentation for releases 6.17 and later is available through the documentation site, with older manuals available for download. User support is directed to GitHub Discussions, and the project offers introductory and methods-focused screencasts.

modeFRONTIER automates engineering simulation workflows and design optimisation by connecting third-party CAD/CAE software with in-house solvers. It combines multiple engineering disciplines in unified workflows and allows several optimisation strategies in one project file. Design exploration includes design of experiments, response surface models, multi-objective optimisation, robust design, reliability analysis and uncertainty quantification. Surrogate models, machine-learning algorithms and reduced-order models support data-driven modelling; ESTECO identifies pilOPT as its proprietary self-adaptive optimisation algorithm. Integrations include parametric solvers and custom connectors for Abaqus, Adams, ANSYS Workbench, CATIA and ANSA. pyFRONTIER and pyCONSOLE connect Python workflows, including scikit-learn and SciPy optimize libraries. Workflows can run in parallel, on local grid computing, or through remote HPC and cloud resources. VOLTA adds collaboration, distributed execution and result traceability. Pricing is on request, with academic licences limited to non-commercial or academic use.