General Electric Company

Genworth Financial, Inc.

Lockheed Martin Corporation

Baker Hughes Oilfield Operations LLC

Naresh Sundaram Iyer

Piero Patrone Bonissone

Weizhong Yan

Rajesh Venkat Subbu

Subhrajit Roychowdhury

Srinivas Bollapragada

Neil Holger White Eklund

Kete Chalermkraivuth

Peter Henry Tu

John Erik Hershey

Christopher Donald Johnson

Dashan Gao

John Michael Lizzi

Kai F Goebel

Kunter Seref Akbay

Ting Yu

James Kenneth Aragones

Kedar Anil Patwardhan

Corey Nicholas Bufi

Yi Yao

Nurali Virani

Xiaohu Ping

Johan Michael Reimann

Viswanath Avasarala

Guoxiang Liu

Robert Carl Lloyd Klenner

Kareem Sherif Aggour

Anil Varma

Steven Hector Azzaro

Hai Qiu

Justin John Gambone, Jr

Feng Xue

Glen Murrell

Anindya Chakraborty

Hayley Stephenson

Mahendra L Joshi

John Anderson Fergus Ross

Lembit Salasoo

Charles Erklin Seeley

Xiaolei Shi

Frederick Wilson Wheeler

Thomas Graham Spears

Rasiklal Punjalal Shah

Alberto Santamaria-Pang

Charles Terrance Hatch

Brock Estel Osborn

James Joseph Schmid

Paulo Ricardo Dos Santos Mendonca

Rogier Sebastiaan Blom

Michael Dean Fullington

Vipul Kumar Gupta

Ruijie Shi

Varish Vyankatesh Mulwad

Charles Larry Abernathy

Jeanette Marie Bruno

Jason Michael Nichols

Mengli Wang

Guangliang Zhao

Matthew Allen Nelson

Alireza Shahkarami

Dewey Lavonne Parkey, Jr

Robert Klenner

Christopher Darby Immer

Chad Eric Knodle

Saikat Ray Majumder

Neil H Eklund

Atul Kshirsagar

Michael Craig Clark

Catherine Joyce Lazatin

Jing Yu

Paul Alex Ardis

Satish Giridhar Iyengar

Ashok Sundaresan

Colin Cranfield

Glenn Richard Murrell

Zhaoyuan Yang

Panqing Gao

Eric Robertson

Yujiao Zheng

Tushar Patil

Seref Akbay Kunter

John Michael Jr Lizzi

Jr Justin Gambone

Kai Frank Goebel

An additive manufacturing apparatus, a computing system, and a method for operating an additive manufacturing apparatus are provided. The method includes obtaining two or more images corresponding to respective build layers at a build plate, wherein each image comprises a plurality of data points comprising a feature and corresponding location at the build plate; removing variation between the features of the plurality of data points; and normalizing each feature to remove location dependence in the plurality of data points.

System and method for additive manufacturing control

An example additive manufacturing apparatus includes an energy source to melt material to form a component in an additive manufacturing process, a camera aligned with the energy source to obtain image data of the melted material during the additive manufacturing process, and a controller to control the energy source during the additive manufacturing process in response to processing of the image data. The controller adjusts control of the energy source based on a correction determined by: applying an artificial intelligence model to image data captured by a camera during an additive manufacturing process, the image data including an image of a melt pool of the additive manufacturing process; predicting an error in the additive manufacturing process using an output of the artificial intelligence model; and compensating for the error by generating a correction to adjust a configuration of the energy source during the additive manufacturing process.

Systems and methods for compression, management, and analysis of downbeam camera data for an additive machine

Briefly, embodiments are directed to a system, method, and article for acquiring a symbol comprising a representation of input data. The symbol may be converted into an emergent language expression in an emergent language via processing of a first neural network. Transmission of the emergent language expression may be initiated over a communications network, where the emergent language comprises a language based on and specific to the input data. The emergent language expression may be translated back into the representation of the input data via processing of a second neural network.

Emergent language based data encryption

A system and method including receiving a set of deep neural networks (DNN) including DNNs trained with an embedded trojan and DNNs trained without any embedded trojan, each of the trained DNNs being represented by a mathematical formulation learned by the DNNs and expressing a relationship between an input of the DNNs and an output of the DNNs; extracting at least one characteristic feature from the mathematical formulation of each of the trained DNNs; statistically analyzing the at least one characteristic feature to determine whether there is a difference between the DNNs trained with the embedded trojan and the DNNs trained without any embedded trojan; generating, in response to the determination indicating there is a difference, a detector model to execute the statistical analyzing on deep neural networks; and storing a file including the generated detector model in a memory device.

Fast identification of trustworthy deep neural networks

Determining a quality score for a part manufactured by an additive manufacturing machine based on build parameters and sensor data without the need for extensive physical testing of the part. Sensor data is received from the additive manufacturing machine during manufacture of the part using a first set of build parameters. The first set of build parameters is received. A first algorithm is applied to the first set of build parameters and the received sensor data to generate a quality score. The first algorithm is trained by receiving a reference derived from physical measurements performed on at least one reference part built using a reference set of build parameters. The quality score is output via the communication interface of the device.

System and methods for determining a quality score for a part manufactured by an additive manufacturing machine

A method for determining a location and trajectory for a new wellbore relative to an adjacent wellbore includes: receiving controllable variable data and uncontrollable variable data related to fracturing a formation by a stimulation operation in a first wellbore penetrating the formation; receiving pressure communication event or pressure non-communication event identification data related to identification of a pressure communication event or pressure non-communication event in a second wellbore penetrating the formation in response to the fracturing; extracting features from the controllable and uncontrollable variable data to provide extracted features; detecting a pressure communication event using the extracted features and the pressure communication event or pressure non-communication event identification data using an analytic technique; identifying one or more quantified causes of the detected pressure communication event using an artificial intelligence technique; and determining the location and trajectory of the new wellbore using the one or more quantified causes.

Process for infill well development in a subsurface reservoir

A method of transferring operational parameter sets between different domains of additive manufacturing machines includes creating a first machine domain parameter set in a first machine domain, accessing a model of a second additive manufacturing in a second machine domain, creating a second machine domain parameter set by applying transfer learning techniques including learning differences between the first machine domain and the second machine domain, adjusting the first machine domain parameter set using the differences before incorporation into the second machine domain to obtain the second machine domain parameter set, the second machine domain parameter set representing operational settings for the second additive manufacturing machine, the second additive manufacturing machine producing a product sample, determining if the product sample is within quality assurance metrics, and if the product sample is not within the quality assurance metrics, adjusting the second machine domain parameter set.

Methods and systems for generating device-specific machine learning model

A method for generating a well completion plan includes: evaluating a plurality of different well completion plans using a reservoir simulator to calculate dynamic flows of fluid through a subsurface formation, each well completion plan having a flow control device with location and associated flow setting or rating, and optionally a packer and location to provide output data for each well completion plan evaluation; developing a surrogate reservoir model using the output data and input data for each well completion plan evaluation; using intelligent sequential sampling of the output and input data for each well completion plan evaluation to provide intelligent sequential sampling data in response to the surrogate reservoir model not meeting a validation criterion; updating the surrogate reservoir model using the intelligent sequential sampling data; and iterating the using and the updating using a latest surrogate reservoir model until the latest surrogate reservoir model meets the validation criterion.

Intelligent optimization of flow control devices

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a Hypothesis Generation Engine (HGE) to receive one or more property target values for a material; a memory for storing program instructions; an HGE processor, coupled to the memory, and in communication with the HGE, and operative to execute program instructions to: receive the one or more property target values for the material; analyze the one or more property target values as compared to one or more known values in a knowledge base; generate, based on the analysis, an initial set of hypothetical structures, wherein each hypothetical structure includes at least one property target value; execute a likelihood model for each candidate material to generate a likelihood probability for each hypothetical structure, wherein the likelihood probability is a measure of the likelihood that the hypothetical structure will have the target property value; convert each hypothetical structure into a natural language representation; execute an abduction kernel on the natural language representation with the at least one likelihood probability, to output at least one proposed structure that satisfies a likelihood threshold for having the property target value; and receive the output of the executed abduction kernel at a testing module to determine whether the output satisfies the property target values. Numerous other aspects are provided.

Autonomous reasoning and experimentation agent for molecular discovery

A system and method for extracting a resource from a reservoir repeatedly alternates between injecting a fluid and injecting a gas into the reservoir. A rate and/or an amount of each of the fluid and the gas that is injected into the reservoir is defined by a first fluid-and-gas ratio function that designates different ratios as a function of time. The ratios designate the rate and/or the amount of the fluid that is injected into the reservoir to the rate and/or the amount of the gas that is injected into the reservoir. The rate and/or the amount at which the fluid and/or the gas is injected into the reservoir is changed according to the ratios designated by the first fluid-and-gas ratio function as time progresses.

System and method for extracting resources from a reservoir through customized ratios of fluid and gas injections

A method of calibrating an additive manufacturing machine includes obtaining a model for the additive manufacturing machine, obtaining a baseline sensor data set for a particular additive manufacturing machine, creating a machine-specific nominal fingerprint for the particular additive manufacturing machine with controllable variation for one or more process inputs, producing on the particular additive manufacturing machine a test-page based object, obtaining a current sensor data set of the test-page based object on the particular additive manufacturing machine, estimating a scaling factor or a bias for each of the one or more process inputs from the current data set, and updating a calibration file for the particular additive machine if the estimated scaling error or bias are greater than a respective predetermined tolerance. A system for implementing the method and a non-transitory computer-readable medium are also disclosed.

Additive manufacturing machine calibration based on a test-page based object

System includes one or more processors that are configured to perform iterations of the following until a predetermined condition is satisfied. The one or more processors are configured to select a modified trial schedule. The modified trial schedule is selected based on initial fluid-extraction data and initial trial schedules and, if available, prior modified trial schedules and prior modified fluid-extraction data from prior iterations. The one or more processors are configured to receive modified fluid-extraction data generated by execution of the modified trial schedule with a designated model of the reservoir. The one or more processors are also configured to update the surrogate model with the modified fluid-extraction data and the modified trial schedule. For at least a plurality of the iterations, the modified trial schedule is selected, at least in part, to reduce uncertainty in a sample space as characterized by the surrogate model.

System and method for generating a schedule to extract a resource fluid from a reservoir

A system includes a schedule generator having one or more processors configured to obtain resource extraction parameters for extracting a resource from a reservoir. The resource extraction parameters include well creation parameters associated with drilling wellbores, well stimulation parameters associated with introducing fracturing fluid into the wellbores, and production parameters associated with extracting the resource through the wellbores. The schedule generator selects initial trial schedules having different values of the resource extraction parameters and receives initial resource output data generated by execution of the initial trial schedules with a designated reservoir model. The schedule generator generates a surrogate model based on the initial resource output data and the initial trial schedules and uses the surrogate model to perform iterations of selecting modified trial schedules until a predetermined condition is satisfied.

System and method for generating a schedule to extract a resource from a reservoir

The system and method disclosed herein provides an integrated and automated workflow, sensor, and reasoning system that automatically detects breaches in protocols, appropriately alarms and records these breaches, facilitates staff adoption of protocol adherence, and ultimately enables the study of protocols for care comparative effectiveness. The system provides real-time alerts to medical personnel in the actual processes of care, thereby reducing the number of negative patient events and ultimately improving staff behavior with respect to protocol adherence.

System and method for protocol adherence

Growing community of inventors

Clifton Park, NY, United States of America

Naresh Sundaram Iyer