Saudi Arabian Oil Company

Kunlunxin Technology (beijing) Company Limited

Baidu USA LLC

Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu.com Times Technology (beijing) Co. Ltd.

Yupeng Li

Peng Lu

Jing Wang

Wei Qi

Chen Feng

Peng Wu

Shouxiang Mark Ma

Liang Gao

Jian Ouyang

Zhao Zhang

Yingze Bao

Le Kang

Yuan Ruan

Xiaozhang Gong

Haoyang Li

Christopher Ayadiuno

Daheng Gao

Maolin Luo

Liang Shan

A method for obtaining geological heterogeneity trends of a geological formation, including the steps: drilling wells (w, w, w, w, w) that penetrate the formation, acquiring well logs (log, log) for each well (w, w, w, w, w) as function of depth interVals (D) of the respectiVe well, determining a third degree tensor (T), where a z-dimension denotes the depths, a x-dimension denotes the well logs, and a y-dimension denotes the wells, extracting matrices (L, L, . . . , L) from the tensor (T), clustering the matrices (L, L, . . . , L) based on the characteristics of the corresponding well logs (log, log) to a clustering result matrix, aggregating the clustering result matrix to a cluster ensemble (π, π, . . . , π), and spatial partitioning the cluster ensemble (π, π, . . . , π) to a map that shows the geological heterogeneity trends associated with cluster types of the wells (w, w, w, w, w).

Method for obtaining geological heterogeneity trends of a geological formation

An electronic device, a method of determining a memory access efficiency for a memory, and a storage medium are provided, which relate to a field of computer technology, and in particular to fields of chip, memory and processor technologies. The electronic device includes: a memory configured to store executable instructions and data to be processed; and a processor configured to execute the executable instructions so as to at least: read a data block to be tested from the memory; determine a memory access description information according to a size information of the data block to be tested; and determine, according to the memory access description information and a channel description information, a memory access efficiency of the processor in reading the data block to be tested, where the channel description information describes a plurality of channels for the processor to read the data to be processed from the memory.

Electronic device, method of determining memory access efficiency for memory, and storage medium

Systems and methods include a method for training machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). Training sample data is generated. Training sample cases are executed using the training sample data. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.

Proxy modeling workflow and program for reactive transport modeling

Systems and methods include a computer-implemented method for random selection and use of observation cells. Observation cells are randomly selected from a model of process-based reactive transport modeling (RTM). The observation cells are incorporated into a neural network for proxy modeling. A set of parameter-specific proxy models represented by a neural network is trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models, where each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models. 3-dimensional interpolation the observation cells is performed.

Random selection of observation cells for proxy modeling of reactive transport modeling

Systems and methods include a method for providing, for presentation to a user, a graphical user interface (GUI) for defining and generating machine learning-based proxy models as surrogates for process-based reactive transport modeling (RTM). User selections of training parameters for generating training sample data are received. The training sample data is generated in response to receiving a parameter files generation indication. A training cases generation indication is received. Training sample cases are executed using the training sample data. User selections of proxy models training parameters are received. A set of parameter-specific proxy models represented by a neural network are trained. Each parameter-specific proxy model corresponds to a specific RTM parameter from a set of RTM parameters. Blind tests are performed using the set of parameter-specific proxy models. Each blind test tests a specific one of the parameter-specific proxy models. Predictions are generated using the set of parameter-specific proxy models.

User interface for proxy modeling of reactive transport modeling

A method and apparatus for executing an instruction are provided. In the method, an instruction queue is first generated, and an instruction from the instruction queue in preset order is acquired. Then, a sending step including: determining a type of the acquired instruction; determining, in response to determining that the acquired instruction is an arithmetic instruction, an executing component for executing the arithmetic instruction from an executing component set; and sending the arithmetic instruction to the determined executing component is executed. Last, in response to determining that the acquired instruction is a blocking instruction, a next instruction is acquired after receiving a signal for instructing an instruction associated with the blocking instruction being completely executed.

Method and apparatus for executing instructions including a blocking instruction generated in response to determining that there is data dependence between instructions

A neural network data processing apparatus includes: an instruction parsing module, configured to split a DMA task into multiple subtasks and acquire configuration information of a data sub-block corresponding to each subtask, where the subtasks are in a one-to-one correspondence with data sub-blocks of transported neural network data; a data reading module, configured to read a first data sub-block according to the configuration information, where the first data sub-block is a data sub-block among data sub-blocks corresponding to multiple subtasks; a data processing module, configured to compress the first data sub-block; a data write-out module, configured to output compressed data resulting from the compression of the first data sub-block.

Neural network data processing apparatus, method and electronic device

Described herein are systems and methods that allow for dense depth map estimation given input images. In one or more embodiments, a neural network model was developed that significantly differs from prior approaches. Embodiments of the deep neural network model comprises more computationally efficient structures and fewer layers but still produces good quality results. Also, in one or more embodiments, the deep neural network model may be specially configured and trained to operate using a hardware accelerator component or components that can speed computation and produce good results, even if lower precision bit representations are used during computation at the hardware accelerator component.

Stereo camera depth determination using hardware accelerator

Display screen with model training graphical user interface

The present application discloses a method and apparatus for operating a field-programmable gate array (FPGA) board in a driverless vehicle. The method according to a specific embodiment includes: collecting driving scenario information on a driving scenario of the driverless vehicle; determining, based on the driving scenario information, a speed at which the driverless vehicle executes a computing operation in the driving scenario; comparing the speed with a speed threshold; switching a working mode of the FPGA board in the driverless vehicle executing the computing operation to reduce power consumption of the FPGA board, in response to the speed being lower than the speed threshold. This embodiment implements the adaptive adjustment of the working mode of the FPGA board, thereby reducing the overall power consumption.

Growing community of inventors

Beijing, China

Yupeng Li