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FLUID FLOW SIMULATION

Resumen de: EP4510034A1

A method of using a computer implemented neural network for a simulation of aerodynamic performance of a technical object having a geometry, the method comprising:Training the neural network using a plurality of sets of encodings of pre-computed computational fluid dynamics, CFD, outputs, wherein the training is generated using inputs comprising:a geometry of at least one training technical object;spatial locations of input nodes of the neural network as node attributes;a relationship between the geometry of the at least one training technical object and the neural network input node locations;associated boundary conditions;operating conditions; andcomputed outputs comprising flow fields and aerodynamic performance parameters;the training using a loss function that evaluates an error between a neural network output and the pre-computed CFD outputs to produce a trained neural network;using the trained neural network with new inputs to generate as output a predicted aerodynamic performance of the technical object, wherein the relationship between the geometry of the at least one training technical object and the neural network input node locations comprises a vector with at least two parameters.

INPUT DATA TRANSFORMATION FRAMEWORK FOR LOW-VOLTAGE MODEL

Resumen de: US2025053802A1

Aspects of the invention include techniques for improving the accuracy of access-limited neural network inference in low-voltage regimes. A non-limiting example method includes training a first machine learning model to perform input transformation for reducing low-voltage bit errors for a deep neural network operating in a low-voltage regime. The training includes inputting training data into the first machine learning model such that, in response, the first machine learning model produces transformed training data; inputting the transformed training data into a clean machine learning model and into perturbed machine learning models, the perturbed machine learning models being generated by applying random bit errors to the clean machine learning model; and optimizing the first machine learning model based on a comparison of output of the clean machine learning model and of the perturbed machine learning models compared to groundtruth labels for the training data.

METHOD AND DEVICE FOR ON-DEVICE LEARNING BASED ON MULTIPLE INSTANCES OF INFERENCE WORKLOADS

Resumen de: US2025053807A1

The present disclosure relates to a method of training a neural network using a circuit comprising a memory and a processing device, an exemplary method comprising: performing a first forward inference pass through the neural network based on input features to generate first activations, and generating an error based on a target value, and storing the error to the memory; and performing, for each layer of the neural network: a modulated forward inference pass; before, during or after the modulated forward inference pass, a second forward inference pass based on the input features to regenerate one or more first activations; and updating one or more weights in the neural network based on the modulated activations and the one or more regenerated first activations.

COMPUTE OPTIMIZATION MECHANISM FOR DEEP NEURAL NETWORKS

Resumen de: US2025053797A1

An apparatus to facilitate compute optimization is disclosed. The apparatus includes a at least one processor to perform operations to implement a neural network and compute logic to accelerate neural network computations.

GENERATING INTEGRATED CIRCUIT FLOORPLANS USING NEURAL NETWORKS

Resumen de: US2025053714A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the node to be placed at the time step to a position from the plurality of positions using the score distribution.

EFFICIENT NEURAL NETWORKS WITH ELABORATE MATRIX STRUCTURES IN MACHINE LEARNING ENVIRONMENTS

Resumen de: US2025053814A1

A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method of embodiments, as described herein, includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.

CRITICAL NODE DETECTION

Resumen de: WO2025034710A1

A computer-implemented method for optimizing neural networks by detecting critical and non-critical nodes is disclosed. The method involves obtaining a neural network comprising a plurality of nodes and their weighted connections. Critical nodes, which have a greater correlation to the network's output than non-critical nodes, are identified through a two-step detection process. The first critical node detection process identifies critical nodes based on weighted direct connections among the nodes. The second critical node detection process identifies critical nodes based on unweighted direct and indirect connections. The configuration of the neural network is then adjusted based on the identified critical and non-critical nodes to improve efficiency or reduce size.

METHOD FOR A WEB SCRAPING TOOL AND CLASSIFICATION ENGINE

Resumen de: US2025045323A1

A web scaping system configured with artificial intelligence and image object detection. The system processes a web page with a neural network to perform object detection to obtain structured data, including text, image and other kinds of data, from web pages. The neural network allows the system to efficiently process visual information (including screenshots), text content and HTML structure to achieve good quality and decrease extraction time.

STOCHASTIC OPTIMIZATION USING MACHINE LEARNING

Resumen de: US2025045577A1

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing stochastic optimization using machine learning. One of the methods includes obtaining data defining a multi-stage stochastic optimization (MSSO) problem instance, the data characterizing an observation distribution, an action space, and a cost function; generating a neural network input characterizing the MSSO problem instance from the data; providing the neural network input as input to a neural network that generates, from the network input, a neural network output characterizing parameters of a value function corresponding to the MSSO problem instance; processing the neural network input using the neural network to generate the neural network output; obtaining a new observation determined according to the observation distribution for the MSSO problem instance; determining, using the value function characterized by the network output, an optimal action to take in response to the new observation; and executing the optimal action.

METHOD FOR PREDICTION OF SYSTEM-WIDE FAILURE DUE TO SOFTWARE UPDATES

Resumen de: US2025045035A1

A device and method for predicting a system failure from update data comprising two or more unimodal modules configured to determine feature information regarding an update to a code section. A neural network is trained with a machine learning algorithm to predict a system failure probability for the code update data that modifies the code section. The trained neural network is provided with the feature information from the two or more unimodal modules.

METHOD FOR PREDICTION OF SYSTEM-WIDE FAILURE DUE TO SOFTWARE UPDATES

Resumen de: WO2025029356A1

A device and method for predicting a system failure from update data comprising two or more unimodal modules configured to determine feature information regarding an update to a code section. A neural network is trained with a machine learning algorithm to predict a system failure probability for the code update data that modifies the code section. The trained neural network is provided with the feature information from the two or more unimodal modules.

SPOKEN LANGUAGE CONTROL SYSTEM

Resumen de: WO2025029631A2

System and techniques for processing spoken language to use as input to a control system are described herein. After an utterance is obtained from a user in a general language, a generative neural network model is invoked on the utterance to transform the utterance into a phrase that conforms to a domain-specific language. The domain specific language phrase is provided a control system that accepts phrases of the domain-specific language as input and controls a device based on the input.

SYSTEMS AND METHODS FOR COMPUTING FEATURING SYNTHETIC COMPUTING OPERATORS AND COLLABORATION

Nº publicación: AU2023309554A1 06/02/2025

Solicitante:

SUN & THUNDER LLC
SUN & THUNDER, LLC

Resumen de: AU2023309554A1

Disclosed is an approach to implement a synthetic engagement system for process-based problem solving. One variant includes : a computing system comprising one or more operatively coupled computing resources; and a user interface operated by the computing system and configured to engage a human operator in accordance with a predetermined process configuration toward an established requirement based at least in part upon one or more specific facts; wherein the user interface is configured to allow the human operator to select and interactively engage one or more synthetic operators operated by the computing system to proceed through the predetermined process configuration, and to return a result to the human operator selected to at least partially satisfy the established requirement; and wherein each of the one or more synthetic operators is informed by a convolutional neural network informed at least in part by historical actions of a particular actual human operator.