Terrafuse, Inc.

C3.ai, Inc.

Adrian Albert

Mehdi Maasoumy Haghighi

Brian White

Mayur Mudigonda

Alok Singh

Ashray Manepalli

Jing Li

Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing, by the one or more computing devices, the observation data and the numeric simulation data, including training a spatial-temporal emulator model for a physical numerical model using the observation data, the numeric simulation data, and the domain interpretable data. The method further includes estimating one or more physical parameters based on the trained spatial-temporal emulator model, compressing the trained spatial-temporal emulator model, including generating candidate mutations of an architecture of the trained spatial-temporal emulator model, evaluating a performance of each of the candidate mutations of the architecture on validation data using metrics, retaining a subset of candidate mutations exhibiting best performance on the metrics, iterating until convergence to a desired reduction in size, and deploying the compressed trained spatial-temporal emulator model to one or more edge devices.

Estimating physical parameters of a physical system based on a spatial-temporal emulator

Apparatuses, methods, and systems for generating gridded predictions of a probability of wildfire spread for geographies are disclosed. One method includes sensing observational climate and earth surface data, obtaining historical data on wildfire spread events, obtaining gridded climate data, creating a set of input features, creating a gridded wildfire data set, training a model that learns one or more probabilistic mapping function emulators between the set of input features and the gridded wildfire data set, which predicts a first probability of wildfire occurrence and a rate and extent of wildfire spread within a geographical region and at a specified period of time, and generating gridded wildfire prediction data including a second probability of wildfire occurrence and spread within the geographical region, using the model and a new set of input features over the geographical region but for a different time period.

Method and system for predicting wildfire hazard and spread at multiple time scales

A computer system receives customer records listing customer attributes and an adoption status of the customer, such as whether the customer has enrolled in a particular energy efficiency program. An initial set of patterns are identified among the customer records, such as according to a decision tree. The initial set is pruned to obtain a set of patterns that meet minimum support and effectiveness and maximum overlap requirements. The patterns are assigned to segments according to an optimization algorithm that seeks to maximize the minimum effectiveness of each segment, where the effectiveness indicates a number of customers matching the pattern of each segment that have positive adoption status. The optimization algorithm may be a bisection algorithm that evaluates a linear-fractional integer program (LFIP-F) to iteratively approach an optimal distribution of patterns.

Predictive segmentation of customers

Apparatuses, methods, and systems for increasing a spatial resolution of gridded spatial-temporal data on weather and climate-related physical variables are disclosed. One method includes obtaining weather and climate data including at least a coarse resolution and a fine resolution, or observational data that includes physical data, pre-processing the weather and climate data, training one or more probabilistic downscaling mapping functions of the at least one of the gridded numeric simulation data or the observational data comprising applying interpolation filters to successively interpolate the pre-processed weather and climate data to generate output data having a resolution that is equal to the fine resolution, and generating high-resolution physical parameters for at least one of a plurality of applications utilizing the trained probabilistic downscaling mapping functions receiving different weather and climate input data that has different times or locations than the pre-processed weather and climate data used in the training.

Method and system for increasing the resolution of physical gridded data

Apparatuses, methods, and systems for generating simulations of physical variables of a physical system are disclosed. A method includes fusing observation data and numeric simulation data. The fusing includes preprocessing the observational data and the numeric simulation data to remove inconsistencies of the observational data and the numeric simulation data, processing the preprocessed observational data and the numeric simulation data to extract interpretable structures and patterns within that data using ground truth and labeled information to create domain interpretable data, normalizing the preprocessed observation data, the numeric simulation data, and the domain interpretable data layers. The method further comprises training a spatial-temporal emulator model for a physical numerical model using the normalized preprocessed observation data, the numeric simulation data, and the domain interpretable data, incorporating prior knowledge of the physical system into the spatial-temporal emulator model, estimating physical parameters of the physical system based on the spatial-temporal emulator model.

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San Francisco, CA, United States of America

Adrian Albert