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The Project Cost Estimating Capability (PCEC) is a parametric tool for estimating the cost of unmanned spacecraft, landers, launch vehicles, crewed systems, and in-space transportation systems. It is an Excel add-in with a simple, robust, and transparent collection of cost-estimating relationships (CERs), their associated statistics, work breakdown structures, estimating templates, and other cost estimating artifacts. PCEC facilitates the investigation, selection, and use of these artifacts towards the creation of a parametric estimate for a space flight hardware system in an Excel workbook. For more information regarding PCEC, please visit: https://www.nasa.gov/ocfo/ppc-corner/pcec-project-cost-estimating-capability/

The Data Service Provider Cost Estimation Tool (CET) and Comparables Database (CDB) package provides NASA's Earth Science Enterprise (ESE) the ability to make lifecycle cost estimates for the implementation and operation of the data service providers that are required to support its science and applications programs. The Data Service Provider CET and CDB package employs a cost-estimation-by-analogy approach. For more information on the package, please visit: http://opensource.gsfc.nasa.gov/projects/CET/index.php

The Mission Operations Cost Estimation Tool (MOCET) is a model developed by the Aerospace Corporation in partnership with NASAs Science Office for Mission Assessment (SOMA). MOCET provides a new capability to generate cost estimates for the operational, or Phase E, portion of NASA science missions. The model implements new Cost Estimating Relationships (CERs) that were derived from historical data for various mission operation stages as applicable to the Planetary Science, Earth Science, and Astrophysics/Heliophysics Explorer missions.

NICM is a probabilistic cost and schedule estimating tool. NICM has proven instrument cost and schedule modeling capabilities that provide probabilistic estimates at both the system and subsystem level for many different instrument types. NICM is used by all NASA centers to support agency-wide proposal activities and program-directed missions.

The Multi-Level Monte Carlo with Python (MLMCPy) software package is code written in Python to solve uncertainty propagation problems. Multi-level Monte Carlo (MLMC) is an efficient alternative to standard Monte Carlo simulation for estimating expectations of outputs to computational models with uncertain input parameters. MLMC greatly reduces computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and high cost. MLMCPy is a parallel, efficient, and modular implementation of the MLMC method that provides a straightforward means of applying the method to general uncertainty propagation problems.

The COTS camera and computer-based star tracker (CBST) software package is a collection of algorithms that take pictures of stars and then output the inertial attitude of the camera. The algorithms' have demonstrated accuracy better than 0.25 degrees when processing imagery taken on-orbit. The algorithms have been developed to be easily and rapidly deployed on a variety of common low-cost and SWaP single board computers and used with COTS cameras. This enables high quality attitude information to be gathered from small, low-cost components that already exist on many spacecraft, which can improve navigation performance and/or redundancy for minimal additional cost and lead time.

SLiC has been used in a variety of projects and missions at JPL, with over 75 active users at JPL alone. It is the official code counter endorsed by the Software Quality Improvement Project for its metrics collections effort across JPL and SQIs most requested software product. SLiC provides data for cost models used during all major JPL pre-Phase A software estimation activities as well as cost validation activities throughout project lifecycles. SLiC is used to gather metrics for the JPL State of Software report to measure process trends in flight projects and multimission ground system services.

Multi-system, multi-decade human spaceflight architectures can be very complex. Using a system architecting approach, the Programmatic Cost Tool (PCT) transforms user-provided data artifacts (e.g., systems, flight types, flight schedules) on human spaceflight architectures into a table of year-by-year programmatic costs consisting of development, production, and sustainment costs for each system/program. From this table, the software automatically generates a standard programmatic view called a 'sand chart. One anticipated use of the PCT, then, is to filter out those architectures in an analysis of alternatives (AoA) that clearly do not meet the affordability test. Another is to show the comparative programmatic costs over time for multiple specific architectures. These kinds of analyses are especially helpful when applied early in the architecture definition process. Fortunately, the PCT can be used in early studies, since the input data requirements are at a relatively high level.

This algorithm is an adaptation of NEAT (NeuroEvolution of Augmenting Topologies), originally designed for optimizing weights and topologies of neural networks, but repurposed to instead find graphs with 2D spatial meaning. It uses the core ideas behind NEAT for evolving solutions stochastically, and can in principle work with any abstract loss/fitness function, so long as the evaluation is fast enough for the algorithm to make progress.