Real-time dynamic optimization of a process model in an online model-based process control computing environment. A mixed integer nonlinear programming (MINLP) solver utilizes a switch to activate and deactivate a first-principle model of a process unit. The switch enables MINLP behavior by attaching to the first-principle model.

A data processing method related to the field of artificial intelligence includes adding an architecture parameter to each feature interaction item in a first model, to obtain a second model, where the first model is a factorization machine (FM)-based model, and the architecture parameter represents importance of a corresponding feature interaction item; performing optimization on architecture parameters in the second model to obtain the optimized architecture parameters; and obtaining, based on the optimized architecture parameters and the first model or the second model, a third model through feature interaction item deletion.

Disclosed is a method of generating a functional singularity at a point or collection of points. The method may include determining a relationship between one or more parameters associated with a physical structure and a spatial gradient of field values of at least one of electromagnetic energy, sound energy, particle beam, or water waves manipulated by the physical structure, configuring, according to the relationship, the spatial gradient of field values to represent a functional singularity at a point, performing backpropagation using the spatial gradient of field values to obtain design parameters corresponding to values for the one or more parameters that achieve the functional singularity at the point, and producing a physical structure having the design parameters.

A modal superposition method using a response dependent non-linear mode concept for a vibration analysis of non-linear engineering structures is provided. The modal superposition method is provided to find steady state response of non-linear systems in frequency domain. The modal superposition method is used in many mechanical structures, especially in design of aerospace and automotive structures, defense industry platforms, steam and gas turbines and mechanical structures containing non-linear forces such as gas turbine engines and jet engines.

A modal superposition method using a response dependent non-linear mode concept for a vibration analysis of non-linear engineering structures is provided. The modal superposition method is provided to find steady state response of non-linear systems in frequency domain. The modal superposition method is used in many mechanical structures, especially in design of aerospace and automotive structures, defense industry platforms, steam and gas turbines and mechanical structures containing non-linear forces such as gas turbine engines and jet engines.

An Ising machine having a network of resistively coupled circuit nodes where at least one node comprises a capacitor whose voltage between its terminals represents a state variable of the node and the voltage is resistively coupled to at least one other node in the network and a two-terminal active electronics element connected in parallel with the capacitor supplying energy to the node, and the element having an odd-symmetric current-voltage characteristic exhibiting a negative current gradient for voltages across its terminals that are below a predetermined threshold value in magnitude, and a positive gradient otherwise and zero current for three voltage instances: zero volts, +V.sub.1 volts, and −V.sub.1 volts, where V.sub.1 is a constant greater than the predetermined threshold.

An Ising machine having a network of resistively coupled circuit nodes where at least one node comprises a capacitor whose voltage between its terminals represents a state variable of the node and the voltage is resistively coupled to at least one other node in the network and a two-terminal active electronics element connected in parallel with the capacitor supplying energy to the node, and the element having an odd-symmetric current-voltage characteristic exhibiting a negative current gradient for voltages across its terminals that are below a predetermined threshold value in magnitude, and a positive gradient otherwise and zero current for three voltage instances: zero volts, +V.sub.1 volts, and −V.sub.1 volts, where V.sub.1 is a constant greater than the predetermined threshold.

The invention relates to a computer-implemented system and method for providing data science as a service (DSaaS) using a real time data prediction contest. Participants in the real time data prediction contest are permitted to execute and submit algorithms, utilize third party data sources, and utilize sub-contests to generate data predictions for the data prediction contest. The participants in the data prediction contest may be humans or software robots. A category of sponsor confidential information related to the data prediction is defined and maintained as confidential by the sponsor, while various methods are implemented to obtain relevant algorithms and data for the data prediction. The sponsor receives data predictions from the participants on a real time or near real time basis, calculates a score for the data predictions, and compensates participants according to their score.

The invention relates to a computer-implemented system and method for providing data science as a service (DSaaS) using a real time data prediction contest. Participants in the real time data prediction contest are permitted to execute and submit algorithms, utilize third party data sources, and utilize sub-contests to generate data predictions for the data prediction contest. The participants in the data prediction contest may be humans or software robots. A category of sponsor confidential information related to the data prediction is defined and maintained as confidential by the sponsor, while various methods are implemented to obtain relevant algorithms and data for the data prediction. The sponsor receives data predictions from the participants on a real time or near real time basis, calculates a score for the data predictions, and compensates participants according to their score.

A computing device, including memory, an accelerator device, and a processor. The processor may generate a plurality of data packs that each indicate an update to a variable of one or more variables of a combinatorial cost function. The processor may transmit the plurality of data packs to the accelerator device. The accelerator device may, for each data pack, retrieve a variable value of the variable indicated by the data pack and generate an updated variable value. The accelerator device may generate an updated cost function value based on the updated variable value. The accelerator device may be further configured to determine a transition probability using a Monte Carlo algorithm and may store the updated variable value and the updated cost function value with the transition probability. The accelerator device may output a final updated cost function value to the processor.