Abstract: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.

Abstract: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.

Abstract: Systems, computer program products, and computer-implemented methods for visualizing and interacting with a plurality of models that collectively represent a real-world system depicted in a graphical user interface. The system described herein may generate an interactive graphical user interface with multiple modes and a plurality of panels associated with the plurality of models. These panels and modes lay the framework for how a user can properly visualize and analyze the models at a particular point in time or over a period of time. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of models that collectively represent a real-world system be interacted with and visualized by a graphical user interface.

Abstract: A computer-implemented method is provided to predict one or more expected quantities using a machine learning model. The method system may comprise steps to receive a set of data items associated with one or more characteristics, generate or train a machine learning model using the set of data items and associated characteristics, receive one or more sets of simulation parameters from a user indicating a hypothetical scenario and a time period, and generate user interface data. The user interface data may comprise a time-based chart illustrating the respective time periods. The computing system may further apply machine learning model to the set of simulation parameters to predict a set of expected quantities based on the simulation parameters, aggregate one or more types of expected quantities from the set of expected quantities to determine one or more combined quantities, and include in the user interface indications of the one or more combined quantities.

Abstract: Systems, computer program products, and computer-implemented methods for visualizing and interacting with a plurality of models that collectively represent a real-world system depicted in a graphical user interface. The system described herein may generate an interactive graphical user interface with multiple modes and a plurality of panels associated with the plurality of models. These panels and modes lay the framework for how a user can properly visualize and analyze the models at a particular point in time or over a period of time. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of models that collectively represent a real-world system be interacted with and visualized by a graphical user interface.

Abstract: Systems, computer program products, and computer-implemented methods for visualizing and interacting with a plurality of models that collectively represent a real-world system depicted in a graphical user interface. The system described herein may generate an interactive graphical user interface with multiple modes and a plurality of panels associated with the plurality of models. These panels and modes lay the framework for how a user can properly visualize and analyze the models at a particular point in time or over a period of time. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of models that collectively represent a real-world system be interacted with and visualized by a graphical user interface.

Abstract: Systems, computer program products, and computer-implemented methods for determining relationships between one or more outputs of a first model and one or more inputs of a second model that collectively represent a real world system, and chaining the models together. For example, the system described herein may determine how to chain a plurality of models by training an artificial intelligence system using the nodes of the models such that the trained artificial intelligence system predicts related output and input node connections. The system may then link related nodes to chain the models together. The systems, computer program products, and computer-implemented methods may thus, according to various embodiments, enable a plurality of discrete models to be optimally chained.