Abstract: A methodology for correlated histogram clustering for machine learning which does not require a priori knowledge of cluster numbers, which extends beyond bimodal scenarios to multimodal scenarios, and does not need iterative optimization methods nor require powerful data processing.

Abstract: Disclosed are systems and methods for operations and maintenance (O&M) systems for controlling the operation of a second system, wherein the second system includes a plurality of objects (i.e., components and/or subsystems). The O&M system comprises means for evaluating at least one of the plurality of objects using a first model to produce a first prediction of a characteristic of the object; evaluating at least one of the plurality of objects using a second model to produce a second prediction of the characteristic of the object, the second model being dissimilar to the first model; generating a final prediction of the characteristic of the object as a function of dynamic weightings of the first prediction and the second prediction; and, executing an operational or maintenance decision with respect to the second system as a function of the final prediction of the characteristic of the at least one of the plurality of objects.

Abstract: Systems and methods for adaptively optimizing a performance function for a system. In one embodiment, an apparatus is configured to select an independent variable that determines an operating state of the system described by the performance function being discontinuous, establish a range of values for the independent variable from data from the system, select a set of random values within the range of values for the independent variable, and evaluate the performance function with the set of random values for the independent variable to provide extrema results for the performance function. The apparatus is further configured to optimize the performance function by selecting a value for the independent variable from the set of random values that provides an extremum result from the extrema results to produce an optimized performance function, and utilize the optimized performance function to control the system in response to real-time data from the system.

Abstract: A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes incorporating measures of merit into system requirements for an aerospace vehicle system to provide aggregated system requirements and measures of merit, and receiving design parameters of the aerospace vehicle system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the design parameters with the generative artificial intelligence system on the processor to produce a design for an operation of the aerospace vehicle system to meet the aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.

Abstract: A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes incorporating measures of merit into system requirements for a commercial operations system to provide aggregated system requirements and measures of merit, and receiving design parameters of the commercial operations system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the design parameters with the generative artificial intelligence system on the processor to produce a design for an operation of the commercial operations system to meet the aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.

Abstract: An operations and maintenance system for unique objects that includes a database subsystem that stores first and second distinct digital twins for each of the unique objects; at least one of the digital twins has an identifier that uniquely associates it with one of the unique objects and defines a unique virtual representation thereof. A virtual sensor subsystem emulates operational data for ones of the objects; the virtual sensor subsystem is not dependent on sensors physically associated with ones of the unique objects. A digital twin comparison subsystem compares outputs of the first and second distinct digital twins for each of the unique objects; the output of at least one of the first and second digital twins is a function of the emulated operational data for its associated object, and the system makes an operational or maintenance decision with respect to an object as a function of the comparison.

Abstract: Systems and methods for the automatic routing of an object from an origin to a destination. In one embodiment, the method includes modeling a route as a fluid dynamics potential flow. The object is an analogue of a flow particle, the origin is an analogue of a source, and the destination is an analogue of a sink. Intermediate destinations from the origin to the destination are defined as a stream function (?) defined by irrotational and incompressible potential flow that independently represents a flow phenomenon operable to influence the route. The method includes dynamically calculating an optimal route for the object based on a current location and an aggregate stream function including a sum of each of the flow phenomena acting on the object between the current location and the destination. The method also includes routing the object through the intermediate destinations based on the optimal route to the destination.

Abstract: A methodology for correlated histogram clustering for machine learning which does not require a priori knowledge of cluster numbers, which extends beyond bimodal scenarios to multimodal scenarios, and does not need iterative optimization methods nor require powerful data processing.

Abstract: Systems and methods for adaptively optimizing a performance function for operating a system. A performance function for a system is adaptively optimized, and utilized for the operation of the system, by selecting a first value for a first independent variable that determines an operating state of the system described by a performance function; establishing a first range of values for the first independent variable; selecting a first number of values in the first range of values to test the first independent variable; selecting first random values within the first range of values for the first independent variable based on the first number of values; evaluating the performance function at the first random values; optimizing the performance function by selecting the first value of the first independent variable from the first random values that provides a first extremum value for the performance function.

Abstract: A system, and related method, operable on a processor and memory for predicting and prescribing a treatment to a disease for a patient, configured to collect population data about the disease, build a population and patient model based on the population data, record patient factors of the patient associated with the disease, and anonymize the patient factors by creating synthetic data of the patient factors. The system is also configured to merge the patient factors into the population and patient model, prescribe the treatment to the disease for the patient based on predicted outcomes from the population and patient model, and store the treatment to the disease for the patient in the memory.

Abstract: Systems and methods for the automatic routing of data packets in dynamic networks, wherein at least a portion of a route for a data packet is modeled as a fluid dynamics potential flow characterized by an irrotational velocity field. Each data packet is the analogue of a flow particle, an originating node of the data packet is the analogue of a source, and a destination node of the data packet is the analogue of a sink. Each of one or more nodes intermediate to the originating node and the destination node for a data packet are defined as a stream function (?) which adheres to the definition of irrotational and incompressible potential flow that independently represents a flow phenomenon that can influence the route of the data packet. A route for a data packet is calculated based on its current location and the aggregate stream function comprising the sum of each of the flow phenomena acting on the data packet.

Abstract: An apparatus, system and method for producing a bid price for a service or a good. The apparatus is configured to determine market pricing of an offering derivable from a labor pricing model to estimate a cost of a service, or derivable from a bill of materials model to estimate a cost of a good; determine strategic pricing of the offering derivable from a likelihood of an action being taken and a magnitude of pricing change if that action occurs; determine a competitor evaluation of the offering capturing a view of a customer towards each competitive offering along factors that may incorporate factors beyond solely cost; and aggregate the market pricing, the strategic pricing, and the competitor evaluation to produce a bid price for the offering to win a competition within a confidence interval.

Abstract: A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.

Abstract: A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.

Abstract: A continuous asymmetric risk analysis system and method of operating the same. In one embodiment, the method includes receiving a definition of a risk event of a complex system based on a likelihood and consequence, and prevention and mitigation measures for the risk event, and receiving risk event data. The method also includes creating a model populated with the risk event data, and executing Monte Carlo simulations of the model to produce Monte Carlo results. The method also includes analyzing and aggregating the Monte Carlo results of the likelihood and consequences, and the prevention and mitigation measures for the risk event to create a nominal risk value of the risk event and asymmetric confidence intervals to produce a continuous gradient of outcomes. The method also includes organizing and presenting the outcomes for evaluation, and prescribing an action for the complex system based on a selected outcome.

Abstract: A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes receiving performance metrics for a threat system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the performance metrics with the generative artificial intelligence system on the processor to produce a threat analysis of the threat system to meet the performance metrics in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.

Abstract: A generative artificial intelligence system and method of operating the same to control complex systems. In one embodiment, the method includes incorporating measures of merit into system requirements for a commercial operations system to provide aggregated system requirements and measures of merit, and receiving design parameters of the commercial operations system represented as stochastic variables. The method also includes executing first order physics-based engineering equations of the design parameters with the generative artificial intelligence system on the processor to produce a design for an operation of the commercial operations system to meet the aggregated system requirements and measures of merit in a single iteration improving computational efficiency and reducing power consumption of the processor operating the generative artificial intelligence system.

Abstract: Operations and maintenance (O&M) system, and related methods, for a plurality of objects employing distinct digital twins. The O&M system comprises: a database subsystem for storing first and second distinct digital twins for each of the plurality of objects, each of the distinct digital twins having an identifier that associates it with one of the plurality of objects and which defines a virtual representation thereof. The system further includes a sensor subsystem operative to obtain operational data for the plurality of objects, and a digital twin comparison subsystem operative to compare outputs of the at least first and second distinct digital twins for each of the plurality of objects; the output of each distinct digital twin is a function of the operational data for its associated object, and the O&M system makes an operational or maintenance decision with respect to an object as a function of the comparison.

Abstract: A system and method for constructing an initial mathematical system representation with a combination of terms including mathematical functions with independent variables dependent on an input signal. A first set of known data is input to the initial mathematical representation to generate a output data. The output data of the initial mathematical representation and a second set of known data, correlated to the first set of known data, is fed to a comparator to generate error signals representing differences between output data and correlated members of the second set of known data. A parameter of the combination of terms is iteratively varied to produce a refined mathematical representation of the system until a measure of the error signals is reduced to a value wherein the output data of the refined mathematical representation over a desired range is equivalent to the second set of known data.

Abstract: A distributed analytics system to control an operation of a monitored system, and method of operation thereof, including an architect subsystem and an edge processing device. The edge subsystem includes an edge processing device associated with the monitored system. The architect subsystem is configured to deploy an analytic model to the edge processing device based on characteristics of the monitored system. The edge processing device is configured to receive the analytic model and independently perform predictive and prescriptive analytics on dynamic input data associated with the monitored system, provide control signals to the monitored system according to the predictive and prescriptive analytics, and provide information to the architect subsystem, including monitored system responses to the control signals. The architect subsystem is configured to modify the analytic model to improve system performance of the monitored system.