arXiv:2610.36746v1 Announce Type: new Abstract: Open Data Analysis Tool for Science and Engineering (ODAT-SE) is an open-source Python framework for inverse problems that are solved by repeated forward simulations. It succeeds 2DMAT, which was developed for surface-structure analysis with quantum-beam diffraction and has since been applied to other fields. In version 3, the search algorithms were separated from the application-specific solvers, which became independent packages, and the software was renamed; this paper describes the current version 4.0. The framework connects optimization and sampling algorithms to solvers through a common interface for parameter mapping, constraints, and objective evaluation, so that an existing simulation code can be used with any of the algorithms. Relative to the previously published 2DMAT, it provides two-level MPI parallelism, which distributes candidate evaluations over processes and each evaluation over a parallel solver, checkpoint and continuation facilities, random search, global optimizers from SciPy, tensor-train optimization, and a Bayesian workflow that estimates partition-function ratios, model evidence, effective noise levels, and marginal posterior distributions. We validate the Bayesian workflow against closed-form results for a linear regression, benchmark tensor-train optimization on continuous, binary, and mixed-integer problems, and verify the two-level parallelization by the same optimum for all process decompositions. Taking up the positron-diffraction example of the 2DMAT paper, the workflow identifies the correlated structural solutions and an effective noise scale close to the imposed perturbation, while exposing incomplete convergence of symmetry-related mode weights. Solver packages, templates, and a gallery of examples support the use of the framework as a general platform for inverse-problem analysis.