NUMERICA CORPORATION — Department of Defense SBIR Phase II: MDA20-001
NUMERICA CORPORATION — SBIR Phase II award from Department of Defense.
Phase II SBIR prototype / development signal
- Phase II is where Department of Defense funds deeper R&D after feasibility. Incumbents with Phase II history are serious competitors on adjacent topics.
- Use this award as past-performance context and to map customer organizations for STRATFI/TACFI-style transition planning.
- At $1,509,763, this is a large obligation for typical SBIR Phase sizing — worth reviewing for scope breadth and teaming opportunity.
- Topic code MDA20-001 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $1,509,763
- Agency
- Department of Defense · Missile Defense Agency
- Program / Phase
- SBIR · Phase II
- Topic
- MDA20-001
- Solicitation
- 20.2
- NAICS
- —
- Place of performance
- CO
- Period
- 2022-03-03 → 2024-03-01
Description
Numerica proposes a novel approach to automated factor-based sensitivity analysis in high-dimensional search spaces that leverages recent advances in physics and machine learning utilizing low-rank tensor models and adaptive sampling techniques to overcome the curse of dimensionality for many problems. Based on this technology, we propose to develop an Automated Sensitivity Analysis Product that exercises the algorithm under test (e.g., a mission-critical MDS algorithm such as AEGIS FOM) by searching the potentially huge space of input parameters (model/algorithm parameters, scenario parameters, Monte Carlo realizations, code modifications, etc.) to analyze a specified set of quantities of interest (e.g., to identify optimal performance configurations, sensitivities to input data, performance boundaries, behavioral trends, potential bugs, etc.). To allow deeper insights into the internal details of the algorithm under test, we also propose to incorporate outputs of code execution analysis tools--such as code coverage and profiling tools--in addition to the algorithm’s native outputs. Approved for Public Release | 22-MDA-11102 (22 Mar 22)