A system and method for generating libraries of chemical compounds having desired and specific properties by formulating a reaction-based mechanism that may be powered by several algorithms including but not limited to genetic algorithm, expert iteration algorithms, planning methods, reinforcement learning and machine learning algorithms. The system and method may also provide the process steps by which these optimized products S′ may be synthesized from the reactants R1,R2 and further enables a rapid and efficient search of the synthetically accessible chemical space.

A system and method for generating libraries of chemical compounds having desired and specific properties by formulating a reaction-based mechanism that may be powered by several algorithms including but not limited to genetic algorithm, expert iteration algorithms, planning methods, reinforcement learning and machine learning algorithms. The system and method may also provide the process steps by which these optimized products S′ may be synthesized from the reactants R1,R2 and further enables a rapid and efficient search of the synthetically accessible chemical space.

This disclosure is generally directed to systems and methods for providing a software sensor in a vehicle. In an example embodiment, a determination is made regarding the availability of a feature upgrade to a vehicle and a request may be made (to a cloud computer, for example), for obtaining the feature upgrade. The cloud computer provides an emulation software module based on emulating a first sensor that is unavailable in the vehicle. The feature upgrade may be installed in the vehicle by executing the emulation software module and by use of a second sensor that is available in the vehicle. In an example implementation, the second sensor available in the vehicle is a type of hardware sensor such as, for example, a camera, and the first sensor that is emulated is a different type of hardware sensor such as, for example, an air quality sensor.

Systems and methods include a computer-implemented method for performing well placement and configuration. Two-dimensional (2D) target entry (TE) points are generated in an area of interest (AOI) for wells to be drilled in an oil reservoir, where the 2D TE points are positioned according to a defined well length resolution. A single lateral is designed for each well using the 2D TE points, where each single lateral is designed with a different length, completion zone, azimuth, and orientation. Using the single laterals, a dynamic reservoir simulation is executed for the wells to be drilled in the oil reservoir, including rotating between different three-dimensional (3D) configurations for each 2D TE. A 3D configuration for each 2D TE is selected for each lateral and based on executing the dynamic reservoir simulation.

Systems and methods include a computer-implemented method for performing well placement and configuration. Two-dimensional (2D) target entry (TE) points are generated in an area of interest (AOI) for wells to be drilled in an oil reservoir, where the 2D TE points are positioned according to a defined well length resolution. A single lateral is designed for each well using the 2D TE points, where each single lateral is designed with a different length, completion zone, azimuth, and orientation. Using the single laterals, a dynamic reservoir simulation is executed for the wells to be drilled in the oil reservoir, including rotating between different three-dimensional (3D) configurations for each 2D TE. A 3D configuration for each 2D TE is selected for each lateral and based on executing the dynamic reservoir simulation.

A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

A system includes a memory and a processing device, operatively coupled to the memory, to perform operations including receiving, as input to a trained machine learning model for identifying defect impact with respect to at least one type defect type, data associated with a process related to electronic device manufacturing. The data associated with the process comprises at least one of: an input set of recipe settings for processing a component, a set of desired characteristics to be achieved by processing the component, or a set of constraints specifying an allowable range for each setting of the set of recipe settings. The operations further include obtaining an output by applying the data associated with the process to the trained machine learning model. The output is representative of the defect impact with respect to the at least one defect type.

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N. The method includes generating a set of approximated system variables corresponding to the set of measurements based on the numerical model.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and/or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.