Co-Active Systems Theory

Generalized CoAST is an approach that dynamically selects, tunes, combines and adapts theory, analogs (models), experts (oracles), and data (pereception) to maximize information gain for optimizing (or improving robustness, reliability, resilience) inference (or control, estimation, learning, decisions etc). Not only does CoAST infer from these sources in a way that is better than any source alone but, in reverse, it dynamically adapts the sources.

CoAST derives from a rich history in Optimal and Sequential Experimental Design,  Incremental Online and Active Learning, Active Sampling, Expected Informativeness, Relevance Feedback, and Co-Active Learning. Generalized CoAST extends the classical notion that typically only involve two-way interactions between specific forms, e.g., Co-Active Learning between humans and machines, or models and instrumentation (e.g., "dddas"). It promotes the "co-evolution of multiple subsystems, each benefiting from the others."   

CoAST can be viewed at both the systems and methodological level.

Fig. 2.4

Informative  Optimization Inference and Learning:

Statistical Transport & Inference for Coherent Structures (   

Fluids have coherent structures in turbulence. These features have always been used to describe fluids but they are rarely used to solve inference problems. We've wondered why that is. This area considers patterns emergent in coherent fluids as a means to efficient Inference. Here are some problems we have solved: 

Dynamics and Optimization of Learning Systems  (DOLS)

Sensing and Imaging

Planning and Control



Natural Scales in Hierarchical Uncertainty Quantification and Recursive Estimation

Quantifying Uncertainty for Coherent Structures using Field Coalescence

Learning to Predict Uncertainty using Dynamic Data Driven Deep Learning 

Quantifying Extreme Rare Event Tails (QuERET)

Decisions with imprecise probabilities