Abstract: An embodiment establishes a knowledge base based at least in part on sensor data received from a network. The embodiment generates a predicted performance parameter for a designated entity of the network using a first machine learning algorithm. The embodiment compares the predicted performance parameter to an actual performance parameter and determines whether the actual performance parameter exceeds a threshold difference from the predicted performance parameter. The embodiment generates, responsive to determining that the threshold difference is exceeded, incentive data using a second machine learning algorithm, where the incentive data is representative of an action selected by the second machine learning algorithm using an iterative optimization process, and where the iterative optimization process comprises performing the action and determining that the actual performance parameter approaches the threshold value in response to the action.

Abstract: Embodiments are directed to a computer-implemented method of analyzing a land region that has been decomposed into a plurality of regular or irregular sub-regions. The computer-implemented method includes applying, using a processor system, a feature extraction process that extracts a set of sub-region environmental descriptors for each of the plurality of sub-regions. The processor system applies a similarity analysis to the set of sub-region environmental descriptors to generate groups of the plurality of sub-regions. The processor system creates a plurality of group-based graphs by encoding each of the groups into a corresponding group-based graph. A spatio-temporal neural network is used to train a model based at least in part on the plurality of group-based graphs.

Abstract: A method, computer program product and computer system to generate safety alerts is provided. A processor retrieves a plurality of measurements associated with a location. A processor determines a set of features based on the plurality of measurements. A processor identifies a set of membership functions for the set of features. A processor determines a safety index for the body of water based on the set of membership functions and one or more input value ranges for the set of features. In response to the safety index being above a threshold value, a processor sends an alert to one or more users regarding the location.

Abstract: Embodiments for managing a wave energy converter (WEC) device by one or more processors are described. At least one environmental characteristic associated with a WEC device in a body of water is received. A prediction of wave conditions on the body of water is calculated based on the at least one environmental characteristic. A signal representative of the prediction of wave conditions is generated.

Abstract: Intelligent classification for product pedigree identification are presented. A transaction agreement request may be received from a user. A revised transaction agreement request may be generated based on one or more user profiles, a multi-party entity feedback loop, one or more constraints relating to the transaction agreement request, and a transaction agreement fulfillment requirements of the entity.

Abstract: Embodiments for providing enhanced edge-based forecasting in a computing environment by a processor. Data from received from one or more data sources may be incorporated into a graph neural network. A forecast of one or more future conditions may be generated based the graph neural network using one or more forecasting models.

Abstract: Embodiments for providing expert-in-the-loop training of machine learning models in a computing environment by a processor. A performance of a machine learning model may be learned. Feedback for the machine learning model may be received based on learning the performance the machine learning model, where the feedback includes domain knowledge provided by a domain expert. The machine learning model may be trained or updated based the feedback of the performance of the machine learning model.

Abstract: Embodiments for providing enhanced generative models based assistance for design and creativity in a computing environment by a processor. A partially completed design of an object may be received. A set of recommendations may be generated for completing the partially completed design based on one or more generative models.

Abstract: Embodiments are provided for providing increased efficiency of various industrial systems and processes in a computing system by a processor. One or more anomalies may be monitored and detected for a plurality of processes of an industrial system using a machine learning operation, wherein the one or more anomalies are localized. A diagnosis is generated to address the one or more anomalies.

Abstract: Embodiments for learning semantic description of data based on physical knowledge in a computing environment by a processor. Physical knowledge data and semantic labels associated with data from one or more data sources may be learned. Source attributes of the one or more data sources may be associated with one or more classes and concepts of a plurality of ontologies based on the physical knowledge data and the semantic labels to generate textual descriptors of the data.

Abstract: A processor may collect data from each of two or more stations of a set of stations. The processor may determine a subset of the set of stations that are related. The processor may monitor a residual for a machine learning model for each station in the subset of stations. The processor may detect a change in the operation of a first station of the subset of stations.

Abstract: Embodiments for deep learning for partial differential equation (PDE)-based models by a processor. A trained forecasting model and consistency constraints may be generated using a PDE-based model, a discretization of the PDE-based model, historical inputs the of the PDE-based model, and a representation of consistency constraints to generate a predictive output.

Abstract: Embodiments for using learned physical knowledge to guide feature engineering in a computing environment by a processor. Physical knowledge data associated with a dataset may be learned. The physical knowledge data may be translated into a plurality of features for one or more automated feature engineering models to execute for one or more prediction and monitoring operations, wherein the plurality of features represent relationships between the physical knowledge data.

Abstract: A method and system for outputting a state of a physical system using a calibrated model of the physical system, where the calibrated model is used to generate a model prediction. The system includes a plurality of sensors connected to a routing node are used to monitor measured data of the physical system. A first sensor of the plurality of sensors includes a logic module configured to determine an uncertainty quantification, and to combine the uncertainty quantification with the model prediction to output the state of the physical system.

Abstract: A processor may identify a first task of a set of tasks. The processor may identify features of the first task. The processor may generate a reputation assessment for a first user related to the features of the first task. The processor may match the first user to the first task based on the reputation assessment.

Abstract: A computer-implemented method of generating a machine learning model pipeline (“pipeline”) for a task, where the pipeline includes a machine learning model and at least one feature. A machine learning task including a data set and a set of first tags related to the task are received from a user. It is determined whether a database stores a first machine learning model pipeline correlated in the database with a second tag matching at least one first tag received from the user. Upon determining that the database stores the first machine learning model pipeline, the first machine learning model pipeline is retrieved, the retrieved first machine learning model pipeline is run, and the machine learning task is responded to. Pipelines may also be created based on stored pipelines correlated with a tag related to a tag in the task, or from received feature generator(s) and models.

Abstract: A computing device configured for automatic selection of model parameters includes a processor and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including providing an initial set of model parameters and initial condition information to a model based on historical data. A model generates data based on the model parameters and the initial condition information. After determining whether the model-generated data is similar to an observed data, updated model parameters are selected for input to the model based on the determined similarity.

Abstract: Techniques of facilitating deep learning model rescaling by computing devices. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise: a rescaling component; and a forecasting component. The rescaling component can determine a scaling ratio that maps low mesh resolution predictive data output by a partial differential equation (PDE)-based model for a sub-domain to high-resolution observational or ground-truth data for a domain comprising the sub-domain. The forecasting component can generate high mesh resolution predictive data for the domain with a machine-learning model using input data of the PDE-based model and the scaling ratio.

Abstract: A computer-implemented method, a computer program product, and a computer system for optimally balancing deployment of a deep learning based surrogate model and a physics based mathematical model in simulating a complex problem. One or more computing devices or servers compare results of running the deep learning based surrogate model with results of partially running the physics based mathematical model or with observations. One or more computing devices or severs output the results of running the deep learning based surrogate model as system outputs of simulating the complex problem, in response to determining that the deep learning based surrogate model is reliable. One or more computing devices or servers output results of running the physics based mathematical model as the system outputs of simulating the complex problem, in response to determining that the deep learning based surrogate model is not reliable.

Abstract: Embodiments for dynamically distributing loads in computational rendering in a computing environment. A computational rendering model on a computational rendering to exploit nested recursive parallelism within a heterogenous computing architecture to enable communication overlap, memory transfer, and data and task management, wherein the computational rendering model is developed for the heterogenous computing architecture.