METAMORPH INC — Department of Defense SBIR Phase I: SB142-003
METAMORPH INC — SBIR Phase I award from Department of Defense.
Phase I SBIR feasibility signal
- Phase I awards fund proof-of-concept work. For capture teams, they mark early interest from Department of Defense in a technical approach.
- Watch for Phase II follow-ons from the same firm/topic family — that conversion path is where budgets and transition pressure rise.
- Obligated amount $149,297. Cross-check similar awards in the same agency and technology tags for going-rate context.
- Topic code SB142-003 links this award to a solicitation family — search the same topic stem for incumbents and recompete timing.
- Amount
- $149,297
- Agency
- Department of Defense · Defense Advanced Research Projects Agency
- Program / Phase
- SBIR · Phase I
- Topic
- SB142-003
- Solicitation
- 2014.2
- NAICS
- —
- Place of performance
- TN
- Period
- 2014-09-24 → 2015-09-14
Description
This project will demonstrate the feasibility of extending the probabilistic design verification capabilities of the OpenMETA tool suite with probabilistic programming methods. These extensions will enable correct-by-construction design methodologies in the presence of component, system, environment, and epistemic uncertainties.The OpenMETA model- and component-based design tool suite for the manufacturing-aware design of complex Cyber-Physical Systems (CPS) uses a deterministic model for dynamic simulations, using a causal component model connections, with lumped parameter models represented in Modelica.The current, limited PCC approach allows stochastic modulation of parameters in a Monte Carlo simulation. Thisproject will design and test mechanisms for introducing Probabilistic Dynamics Modeling directly in Modelica, a significant capability enhancement of the Modelica . These extensions will add probabilistic variablesparameters, signals, input/outputs to the model that could vary within a simulation run, and could be used to accurately incorporate the uncertainty in the dynamics models.The current PCC is insufficient for capturingtime-dependent, non-invariant signals. The proposal will add a probabilistic dynamics model to Modelica to address these limitations. Furthermore, the project will investigate mechanisms for addressing Inverse Problems, using inferencing techniques (Bayesian, MCMC), and sampling reduction approaches