Forward and inverse modeling of nondestructive evaluation (NDE) are key needs for optimized, quantitative NDE. Some forward modeling tools exist commercially, but inverse modeling remains a topic mostly in low TRL research. The ill-posed nature of the problem in general requires data-driven methods that are computationally intensive and highly problem specific. We propose two innovations to provide significant improvement to inversion: First, modern classifier-based data reduction, to prepare data for the second innovation, Kriging methods for a generalized NDE inversion approach. Experimental data and/or modeled data can be used to define known points in a multi-dimensional solution space, and Kriging methods can provide efficient interpolation in this space to invert new NDE data. TRI/Austin, AeroMatter, and Computational Tools are teaming to develop and demonstrate inversion of ultrasonic NDE on composite structures for quantitative damage assessment. The proposed innovations to be provided are: 1. Combined use of experiment and model data for developing the known solution points. 2. Dimensional reduction of the data for efficient inversion using state of the art classifier techniques. 3. Kriging methods for interpolation of new NDE data in the solution space. 4. High performance computing (HPC) technologies to speed data reduction and Kriging results. The significance of the innovations are that this approach offers an ability to invert NDE data using known or truth data from experiment and/or models, and is readily adapted to high performance computing technologies for practical use.The NDEInverter will work with the rest of the tools in TRI/Austin's NDEToolbox. NDEToolbox serves as a foundational, evolving platform for the management and analysis of NDE data, interaction with NDE models, and risk / reliability prediction.