Abstract: A system may include at least one processor configured to: obtain a trained neural network (NN); obtain or calculate at least one average feature information associated with the trained NN, each of the at least one average feature information including a given average feature information summarizing in-class statistics that each layer of the trained NN uses for a given class; receive an incoming observation influencing a given layer; calculate a corresponding feature information of the incoming observation, the corresponding feature information summarizing statistics of the incoming observation for the given layer; classify the incoming observation as being in the given class; calculate a distance score associated with a distance between the corresponding feature information of the incoming observation and the given average feature information; determine the incoming observation is an out-of-distribution (OOD) sample; and output an alert indicating the incoming observation is OOD and/or discard the incoming o

Abstract: The present disclosure provides for the generation of microstructural images of components (e.g., titanium alloys) using machine learning frameworks. More particularly, the present disclosure provides for the generation of microstructural images of components (e.g., titanium alloys) as a function of heat treatment conditions using conditional generative adversarial networks. The present disclosure advantageously provides ways to accelerate component designs (e.g., titanium alloy designs) by developing generative models which can produce synthetic yet realistic microstructures conditioned on heat treatment conditions.

Abstract: A system having a set of instructions executable by the system for multi-source machine learning modeling framework for process property mapping of thermoplastic composite manufacturing, the set of instructions comprising: an instruction to select a surrogate machine learning model from a suite of machine learning networks; an instruction to involve uncertainty quantification associated with predictions which provide a quantified estimate of how much the machine learning model can be trusted; an instruction to provide multi-physics process model output to the machine learning model; an instruction to provide heterogeneous data sources for use by the machine learning model; an instruction to determine estimates of optimal process parameters employing budget-constrained multi-fidelity process optimization; an instruction for deployment the multi-source machine learning model in the implementation of carbon fiber reinforced thermoplastic polymer induction welding; and an instruction to perform induction welding

Abstract: The present disclosure relates to a method and apparatus to mitigate thermo-acoustic instabilities in combustor of gas turbine engines using a lean premixed flame; and provides a dynamic control strategy for mitigating thermo-acoustic instability in a swirl stabilized, lean premixed combustor by rotating the otherwise static swirler meant for stabilizing the lean premixed flame. The swirler is subjected to a controlled rotation for imparting increased turbulence intensity and higher tangential momentum to the premixed reactants towards mitigating thermo-acoustic instability. The rotating swirler induces vortex breakdown and increased turbulence intensity to decimate periodic interactions found during a particular phase of the instability cycle on account of periodic collision of diverging flame base with the flame segment above the dump plane in the combustor.