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OUQ (Optimal Uncertainty Quantification)

OUQ is a rigorous mathematical framework in which the UQ objectives and the assumptions or information sets are brought to the forefront. This framework is based on the observation that, given a set of assumptions and information about the problem, there exist optimal bounds on uncertainties: these are obtained as values of well-defined optimization problems corresponding to extremizing probabilities of failure, or of deviations, subject to the constraints imposed by the scenarios compatible with the assumptions and information. In particular, this framework does not implicitly impose inappropriate assumptions, nor does it repudiate relevant information. You can learn more about OUQ, its proven application, and other information by clicking the link/logo.


Pathos is a framework for heterogenous computing. It primarily provides the communication mechanisms for configuring and launching parallel computations across heterogenous resources. Pathos provides stagers and launchers for parallel and distributed computing, where each launcher contains the syntactic logic to configure and launch jobs in an execution environment. Some examples of included launchers are: a queue-less MPI-based launcher, a ssh-based launcher, and a multiprocessing launcher. Pathos also provides a map-reduce algorithm for each of the available launchers, thus greatly lowering the barrier for users to extend their code to parallel and distributed resources. Pathos provides the ability to interact with batch schedulers and queuing systems, thus allowing large computations to be easily launched on high-performance computing resources. One of the most powerful features of pathos is "tunnel", which enables a user to automatically wrap any distributed service calls within a ssh-tunnel. You can learn more about Pathos, its current release information, and the developer at the link above.

Mystic, a simple model-independent inversion framework, provides a collection of optimization algorithms and tools which allow the user to more robustly (and readily) solve optimization problems. All optimization algorithms included in mystic provide workflow at the fitting layer, not just access to the algorithms as function calls. Mystic gives the user fine-grained power to both monitor and steer optimizations as the fit processes are running.

Where possible, mystic optimizers share a common interface, and thus can be easily swapped without the user having to write any new code. Mystic solvers all conform to a solver API, and thus have common method calls to configure and launch an optimization job.

By providing a robust interface designed to allow the user to easily configure and control solvers, mystic reduces the barrier to implementing a target fitting problem as stable code. Enabling the user to focus on building their physical models, and not spend time hacking together an interface to optimization code.  You can learn more about Mystic, its current release information, and the developer at the link above.
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