Neural network 3d simulation. 3). }, month = {nov}, articleno = {220}, numpages = {14}, keywords = {simulation, neural network, dynamics, disentangle, deep learning, cloth, unsupervised} } Jul 5, 2023 · We present the first neural network that has learned to compactly represent and can efficiently reconstruct the statistical dependencies between the values of physical variables at different spatial locations in large 3D simulation ensembles. Animated surfaces are level surfaces of a neural network. In this demonstration you can play with a simple neural network in 3 spacial dimensions and visualize the functions the network produces (those are quite interesting despite the simplicity of a network, just click 'randomize weights' button several times). The suggested approach provides accurate reservoir simulation and history matching for a real-world oilfield 神经网络. On this basis, the construction method of fast instant network communication system based on convolutional neural network and fusion morphological 3D simulation model is studied. 3. Wenbo Zhang1, David S. Oct 1, 2023 · Thus, in this paper we aim to propose a self-adaptive algorithm of physics-informed neural networks for 2D and 3D linear and nonlinear Biot models to overcome the above mentioned difficulties. Reservoir simulation and adaptation problems are considered within a unified framework for neural network optimization. We present our easy, six step programme for learning how to model spiking neural networks with Brian. Firstly, we apply the original PINNs algorithm to 2D and 3D linear and nonlinear Biot models. Then we will teach you step by step how to implement your own 3D Convolutional Neural Network using Keras. Article Google Scholar Malkawi A, Augenbroe G (2004). Jan 15, 2023 · The last decade has seen a rise in the number and variety of techniques available for data-driven simulation of physical phenomena. Aug 29, 2021 · Facing the increasing demand of different industries, how to build an instant network communication system for 3D virtual animation has become a research hotspot. Although Kohonen networks could mesh n-dimensional shapes, they required parameter readjustment via competitive learning each time the network was called learning (ML) techniques, such as articial neural network (ANN), fuzzy logic (FL), and genetic algorithm (GA), have been introduced and broadly utilized in garment e-MC recently [4345– ]. Feb 14, 2020 · Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials and extracting effective mechanical properties. 06. Aug 31, 2023 · This paper presents a novel approach to enhance the simulation accuracy by serving a more accurate power loss model. Graphs represent the state of granular flows and interactions, like the exchange of energy and momentum between grains, and GNN learns the local interaction law. We can use a JavaScript library called "three. Aug 10, 2019 · Agent-based models (ABM) of evolutive artificial neural networks. A large number of sandstone computed tomography (CT) images are used as training input for a fully convolutional neural network model. However, given that NFs are essentially neural networks, it remains unclear whether and how they can be Nov 25, 2019 · [Show full abstract] automated retrieval of 3D context, weather data, building models, and CFD simulations, and uses 3D Convolutional Neural Networks (CNNs) to predict 3D vector flow of wind We provide of detailed analysis of the problem to establish the bases of neural cloth simulation and guide future research into the specifics of this domain. 关注 1554. This model is used to reconstruct the three-dimensional (3D) digital core of Berea sandstone based on a small number of CT images Jun 1, 2023 · The rationality and the high efficiency of the neural network is validated by comparing with the results of the direct numerical simulation (DNS), the large eddy simulation (LES), and the deep Sep 15, 2023 · We propose to accelerate a high order discontinuous Galerkin solver using neural networks. For modeling large-scale urban microclimate problems Dec 8, 2023 · Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. 109332 Corpus ID: 231985622; End-to-end neural network approach to 3D reservoir simulation and adaptation @article{Illarionov2021EndtoendNN, title={End-to-end neural network approach to 3D reservoir simulation and adaptation}, author={E. We include a corrective forcing to a low polynomial order simulation to enhance its accuracy. chromium browsers only. We extend the transmission cross coefficient formula to include the off-axis mask 3D effects. Sacks1(B) Willerson Center for Cardiovascular Modeling and Simulation, Oden Institute for Computational Engineering and Sciences, Department of Biomedical Engineering, University of Texas at Feb 9, 2022 · Deep neural network models have shown great potential in accelerating the simulation of fluid dynamic systems. Based on a data-driven neural network power loss model, the proposed method aims to address the issue of low simulation accuracy resulting from inaccurate input power losses. It is based very loosely on how we think the human brain works. , NeRF 44), which uses a coordinate-based neural network to memorize the entire 3D volume compactly. It's just a feed-forward network with additional units called context neurons. 2, Fig. We can then modify this code to visualize the Feb 20, 2021 · A single neural network model allows a forward pass from initial geological parameters of the 3D reservoir model through dynamic state variables to well's production rates and backward gradient In this paper, we propose a novel 3D convolutional neural network (3D-CNN) to recognize machining fea - tures and to detect feature areas from a 3D CAD model. This special issue focuses on a variety of topics, such as data analysis methods for brain connectivity, the development of brain simulation platforms, spiking neural networks (SNNs) for modeling brain circuits, and applications of SNNs in real-world scenarios. In this article, we will be briefly explaining what a 3d CNN is, and how it is different from a generic 2d CNN. 1, Fig. Aug 1, 2021 · A single neural network model allows a forward pass from initial geological parameters of the 3D reservoir model through dynamic state variables to well’s production rates and backward gradient Jan 4, 2023 · Modeling three-dimensional (3D) turbulence by neural networks is difficult because 3D turbulence is highly nonlinear with high degrees of freedom and the corresponding simulation is memory-intensive. 2, based on a 50x50x16 mm 3 monolithic LYSO crystal coupled to an 8x8 readout array of silicon photomultipliers (SiPMs). Graph. Here is an example of how to create a simple 3D chart using three. The CNN prediction is 5,000 times faster than the electromagnetic simulation. We demonstrate learning and reconstruction with a Objective We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in monolithic scintillation detectors. These problems lead to the frequent communication interruptions and poor stability of 3D UAV networks. org. A neural network tool written from scratch in Rust + WebAssembly for building, training, visualizing, and experimenting with neural networks in the browser Input layer has 2 dimensions, each with a range of [0, 1]. The training data are generated by a Berkeley short-channel IGFET model (BSIM) with ranges of channel lengths, widths, and oxide thicknesses. org, the platform he co-founded. 7 show the errors between the predicted solutions and the true solutions for the 3D forward and inverse problems, comparing with Fig. We can use the eigen value decomposition method to accelerate the calculation. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). 1 Department of Biostatistics, Yale School of Public Health, New Haven, CT, United States. It’s a technique for building a computer program that learns from data. Our formula is applicable to arbitrary source shapes and defocus. emergent provides a toolkit / framework for implementing neural network models, written in Go, with an optional Python interface that is automatically generated from the Go code. The brain neural system is extended to the machine and to the systems of communications. Sep 1, 2011 · A three dimensional (3D) computational fluid dynamics (CFD) simulation and a neural network model are presented to estimate the behaviors of the Colburn factor (j) and the Fanning friction factor Digit recognition training process🔵Simulation available online: (requires sign in) UPDATED https://www. Next, the network is asked to solve a problem, which it attempts to do over and Mar 28, 2020 · Mar 28, 2020. Each layer consists of one residual block with one 3D convolution with stride 2 filters 4x4x4 and one 3D convolution with stride 1 and filter 3x3x3. 3 Simplifying the 3D Model. js. Brian is therefore designed to be easy to learn and use, highly flexible and Mar 24, 2024 · These results indicate the framework’s outstanding prediction performance and remarkable fitting ability. To simulate the fluid flow problems, the particle-method approach based on SPH (Smoothed Particle Hydrodynamics) is used herein. A single neural network model allows a forward pass from initial geological parameters of the 3D reservoir model through dynamic state variables to well's production rates and backward gradient propagation to any model inputs and variables. Introduction. The model implements two key assumptions. Our tests showed these gated blocks improved our results Aug 3, 2022 · One could also replace LDI with recently emerged neural scene representation (i. Applying a physics model simulator-based learning strategy trained entirely on natural image datasets, we show our network can robustly reconstruct complex 3D biological samples. You can also complement PyNEST with PyNN, a simulator-independent set of Python commands to formulate and run neural simulations. We construct the corresponding trial solution using finite element basis functions. js: In this example, we have created a simple 3D chart with a single cube. McDougal 1,2,3* Cameron Conte 2,4,5 Lia Eggleston 6 Adam J. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. We present a neural network (NN)-based transistor modeling framework, which includes drain, source, and gate currents and charges and their variabilities. Nov 22, 2019 · [Show full abstract] automated retrieval of 3D context, weather data, building models, and CFD simulations, and uses 3D Convolutional Neural Networks (CNNs) to predict 3D vector flow of wind Jun 18, 2021 · The neural network generates the corresponding FE nodal values for a trial solution with a given realization of parameters. 3 Neural Network Architecture Our network architecture has a U-net shape with eight encoder and seven decoder layers. Building on this idea, MeshGraphNets [20] has constructed a cloth simulation system based on a graph neural network. Feb 20, 2021 · We present a unified approach to reservoir simulation and adaptation problems. Neural Network 3D Simulation是神经网络工作原理可视化的第3集视频，该合集共计4集，视频收藏或关注UP主，及时了解更多相关视频内容。. The simulator will help you understand how artificial neural network works. com/DigitRecognition/3dnet. be/yyS5hjyOFDoIn this video, we are learning how neural networks work, making our own neural network from scratch, and then training th Therefore, it is of great practical significance to study the algorithm of the 3D virtual animation instant network communication system based on convolution neural networks' algorithm and fusion of the morphological 3D simulation model . One forward and the backward pass of single training example is Neural Network Simulation System. The primary application of emergent is for the biologically-based With these commands, you describe and run your network simulation. Learn computational neuroscience. Our results show it can accurately predict the dynamics of a wide range of physical systems, including Jan 5, 2021 · In this paper, the complete process of constructing 3D digital core by full convolutional neural network is described carefully. Deep learning has also proven successful in complex 3D tasks. Apr 16, 2024 · Now that we have created the neural network, we can create the 3D simulation chart. Newton 1,2,7 Hana Galijasevic 6. Therefore, it is a complex task to develop a correlation between process parameters and printed parts The network takes input of the low-SBR data and reconstructs 3D fluorescent emitters embedded in scattering media. Traditional electromagnetic field (EMF) solvers are inefficient for large-scale technology problems, while deep neural networks rely on a huge amount of expensive rigorously Nov 17, 2023 · The authors noted that few meshing systems using neural networks were capable of generating 2D and 3D meshes without a loss in quality and in less computation time than traditional software. The GPU-implementation consists mainly of the Jan 1, 2022 · We present an end-to-end neural network model for 3D reservoir simulation and production rates calculation. 2012. While you define your simulations in Python, the actual simulation is executed within NEST's highly optimized simulation kernel which is written in C++. In this work, we apply the FNO network for real-time three-dimensional (3D) urban microclimate simulation. This is a part of my project to simulate neural networks interactions in a 3D environments. The paper first introduces the method used to construct the neural network power loss model and automate Dec 30, 2019 · This is the new home of the emergent neural network simulation software, developed primarily by the CCN lab, originally at CU Boulder, and now at UC Davis: https://ccnlab. }, journal = {ACM Trans. In this work, we present a proof-of-concept study of the application Apr 10, 2024 · Background: The increasing demands on computational lithography and computational imaging in the design and optimization of lithography processes necessitate rigorous modeling of EUV light diffracted from the mask. The model fitting and Getting started. The two deep neural networks within the framework can achieve high prediction accuracy in both the training and test sets because their training input and output consist of 3D tensor data comprising time-series road sections. 1016/J. In simulation-based design approaches, a considerable amount of time is spent by repeated simulations. Then, the network is N2 - We propose to use a 3D convolutional neural network to accelerate three-dimensional device simulation by generating an electrostatic potential profile. This article will be written around these 4 parts: English | 中文. Input image: Filter: Weighted input: Mar 25, 2021 · Humans initially learn about objects through the sense of touch, in a process called “haptic exploration. We have decided to completely reboot the entire enterprise from the ground up, with a much more open, general-purpose design and approach. ” In this paper, we present a neural network model of this learning process. •. VoxNet: A 3D Convolutional Neural Network for real-time object recognition. It can be found that most of the research results of scholars in this field are innovations and Apr 1, 2020 · A 3D convolutional neural network is able to create a functional relationship between pore morphology and the steady state solution of the Navier-Stokes equation for laminar flow. Facing the Aug 28, 2021 · Facing the increasing demand of different industries, how to build an instant network communication system for 3D virtual animation has become a research hotspot. Part 2: https://youtu. First, a collection of software “neurons” are created and connected together, allowing them to send messages to each other. The network is trained using backpropagation algorithm, and the goal of the training is to learn the XOR function. In this paper, a three-dimensional deep convolutional neural network (3D-CNN) is proposed to predict the effective material properties for representative volume elements (RVEs) with random May 17, 2022 · Efficient Simulation of 3D Reaction-Diffusion in Models of Neurons and Networks. The first layer of the network (yellow Dec 31, 2020 · Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. The neural network approach opens a broad and convenient way for implementation of many reservoir simulation and adaptation strategies. Tweet Jan 15, 2024 · Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. Mar 29, 2021 · Physical model simulator-trained neural net work for com- (DIP), which reparameterizes the 3D sample reconstruction with an untrained, deep generative 3D convolutional neural network (CNN). js, Three. The video documents the 3D simulation of an artificial neural network realized by Denis Dmitriev for CyberControls. The NNs are trained to learn the geometry dependence. Going beyond linear dependencies, we consider mutual information as a measure of non-linear dependence. A three dimensional (3D) computational fluid dynamics (CFD) simulation and a neural network model are presented to estimate the behaviors of the Colburn factor (j) and the Fanning friction factor (f) for wavy fin - and - flat tube (WFFT) heat exchangers. Our simulation results and quantitative analysis show that SBR-Net can accurately reconstruct emitters located at depths up to one scattering length deep inside the scattering media across a broad range of SBR and scattering Elman Recurrent Neural Network Simulator An Elman network is a simple recurrent network (SRN). If you’re not already familiar with computational neuroscience, we would recommend you get started with some of these freely available online resources: Neuronal Dynamics (Gerstner et al DOI: 10. Comput. In this work we want to go one step further. It involves layer-by-layer deposition via a computer-aided design (CAD) model. Most notably, in our experiments, particles stay within the geometric objects without using handcrafted conditions or restrictions. e. The development of complex simulation models is time-consuming and complicated. Figure 1: Classical simulation scheme with the finite-differences method. The structure of ARINet is composed of 3D 3D Visualization of a Convolutional Neural Network. A CAD model that includes single- and Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. It is written in the Python programming language and is available on almost all platforms. When applied to 3D data, NFs offer a solution to the fragmentation and limitations associated with prevalent discrete representations. In the training phase, the deep neural network is trained with the simulation results for various 3D MOSFETs in a supervised manner. Dec 31, 2020 · 1. 人工智能. The network is trained using backpropagation algorithm, and the goal of the training is to learn a sine function. Li1, Tan Bui-Thanh2, and Michael S. Three-dimensional (3D) printing, under additive manufacturing, is a new, promising field that has gained prevalent attention in all fields [1,2,3,4]. New York, NY: Spon Press. 1016/j. 2023. The model embeds the problem’s governing equations and boundary conditions into the neural network and treats the neural network’s output as the numerical solution of the partial differential equation. jcp. To achieve highly efficient training and prediction, we implement a lightweight 2D network structure that utilizes a multi-channel input for encoding the axial Jan 15, 2024 · Recently, the Fourier Neural Operator (FNO) has been shown to be very promising in accelerating solving the Partial Differential Equations (PDEs) and modeling fluid dynamic systems. It is well known that the human body's own motion is full of strong personality, emotion, and high-dimensional characteristics, leading to the automatic synthesis of diverse and lifelike 3D human motion data continues to be a challenging task. 1. retopall. The first is that haptic exploration can be thought of as a type of navigation, where the exploring hand plays the role of an autonomous agent, and the explored object I Train The Weights of the Neural Network using Evolutionary Algorithm (Genetic Algorithm) At the first I Instantiate N Randomly Cars,When All Car Crashed I Sort The Cars Fitness Value And The Pick Up The Best Cars, to Stay For The Next Generation and then i Make Cross Over For The Best Cars And Mutated it and then The Next Generation Start The Operations Repeat Until The Cars Reach The Goal Jun 9, 2018 · Watch on. Jun 26, 2023 · BGNNs are able to accurately reproduce 3D granular flows within simulation uncertainties over hundreds of thousands of simulation timesteps. 深度学习. The adaptation process for one-year period takes about 3 hours. Therefore, ML technologies integrated with 3D digital simulation, including radial basis function (RBF) ANN, probabilistic neural network (PNN), support Mar 22, 2023 · Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. Illarionov and Pavel Temirchev and Dmitry Voloskov and Ruslan Kostoev and Maxim Simonov and Dimitri Pissarenko and Denis M Oct 7, 2020 · Here we introduce MeshGraphNets, a framework for learning mesh-based simulations using graph neural networks. Feb 2, 2024 · 2. Feb 22, 2024 · To address these issues, we employ a graph neural network (GNN), a novel deep learning technique to develop a GNN-based simulator (GNS) for granular flows. The Mar 29, 2021 · Three-dimensional human motion synthesis is one of the key technologies in the field of computer animation and multimedia applications. We Oct 22, 2021 · Thermal simulations are an important part of the design process in many engineering disciplines. 3D printing has several advantages: (a) it can produce parts with complex shapes, which are difficult to produce using conventional manufacturing Dec 20, 2023 · In recent years, Neural Fields (NFs) have emerged as an effective tool for encoding diverse continuous signals such as images, videos, audio, and 3D shapes. 6, Fig. The suggested approach provides accurate reservoir simulation and history matching for a real-world oilfield Oct 1, 2023 · Fig. This means we have to model complex geometric Oct 1, 2023 · In this paper, we propose a self-adaptive algorithm of physics-informed neural networks (PINNs) for 2D and 3D linear and nonlinear Biot models, including solving the forward and inverse problems. The forcing is obtained by training a deep fully connected neural network, using a high polynomial order simulation but only for a short time frame. 2 Center for Medical Informatics, Yale University, New Haven, CT . 2021. H. Our model can be trained to pass messages on a mesh graph and to adapt the mesh discretization during forward simulation. Neural Network Simulator is a real feedforward neural network running in your browser. Recently, the attention mechanism has been shown as a promising approach to boost the performance of neural networks on turbulence simulation. Works in Chrome & Edge - Animate slows training Road Map - Color connections, visibility - Drag and drop layers together - Import/Export models - More datasets - Visualization tools - Large networks, new layers - Log and rank models - Import and connect datasets - Aesthetics. 3, we see that under the same training time the L 2 relative errors decrease similarly to ones of the linear Biot model, while the L 2 relative errors are slightly higher than ones of the linear model. js and Tween. OR; AND; XOR; 3 x 4; 3 x 4 x 2; 4 x 4 x 4; Random; Tutorial; by Mitch Crowe. Robert A. Here , s0 and ui denote reservoir static variables, initial state and control parameters for the time interval. High-Speed Simulation of the 3D Behavior of Myocardium Using a Neural Network PDE Approach. To improve efficiency and accuracy, simplifying the 3D model by removing unnecessary geometries and defining the trajectory of movement is necessary (Fig. We aim to learn 3D simulations by Graph Neural Networks (GNNs) towards industrially relevant setups, such as hoppers and rotating drums. See the github site for all the code and extensive documentation. Effects of the five geometrical factors of fin pitch, fin height, fin length, fin thickness, and wavy amplitude are investigated over a wide NeuroVis is an interactive Neural Network visualizer and tutorial. Sep 18, 2021 · Neural Networks, 19: 122–134. Four geometric features extracted from the binary image are needed to make the model robust. Advanced Building Simulation. Book Google Scholar Maturana D, Scherer S (2015). Context neurons receive input from the hidden layer neurons. 003 Corpus ID: 97129381; Solid oxide electrolysis cell 3D simulation using artificial neural network for cathodic process description @article{Grondin2013SolidOE, title={Solid oxide electrolysis cell 3D simulation using artificial neural network for cathodic process description}, author={Dominique Grondin and Jonathan Deseure and Patrick Ozil and Jean-Pierre With unmanned aerial vehicles (UAVs) being widely used, the rapidly changing network topology and vertical height changes of UAVs have been bottlenecks for many wild applications, such as battlefield communication. Jan 1, 2022 · We present an end-to-end neural network model for 3D reservoir simulation and production rates calculation. Approach The required data is obtained by Monte Carlo simulation in GATE v8. 112309 Corpus ID: 259811825; Physical information neural networks for 2D and 3D nonlinear Biot model and simulation on the pressure of brain @article{Chen2023PhysicalIN, title={Physical information neural networks for 2D and 3D nonlinear Biot model and simulation on the pressure of brain}, author={Hao Chen and Zhi-hao Ge}, journal={J. 5, Fig. source. At each timestep the simulation process (1) outputs the solution - "End-to-end neural network approach to 3D reservoir simulation and adaptation" Brian is a free, open source simulator for spiking neural networks. Over the past few decades, deep learning has become a successful computing method in many fields because of its ability to deal with highly nonlinear Dec 15, 2020 · In this paper, we present the data-driven fluid flow simulations using the deep CNN (Convolutional Neural Network) with the parametric softsign activation functions. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. CHERD. The Annual Radiation Intensity Neural Network (ARINet) demonstrates the feasibility of using a 3D convolutional neural network to predict the surface radiation received by building façades. Common Neural Network modules (fully connected layers, non-linearities) Classification (SVM/Softmax) and Regression (L2) cost functions; Ability to specify and train Convolutional Networks that process images; An experimental Reinforcement Learning module, based on Deep Q Learning. The drain, source, and gate currents are They showed impressively how well particle simulation trajectories can be learned by Graph Neural Networks (GNNs). _-微末-_. Perception and cognitive learning is here visualised also with the complicity of the soundtrack. Once trained, these models can make inferences within seconds, thus can be extremely efficient. An alternative, fast simulation tool would be a welcome addition to any automatized and simulation-based optimisation workflow. Our tests showed these gated blocks improved our results Sep 24, 2023 · This paper uses the physical information neural network (PINN) model to solve a 3D anisotropic steady-state heat conduction problem based on deep learning techniques. TensorSpace is a neural network 3D visualization framework built using TensorFlow. The neural network-based surrogate is trained by minimizing the sum of the energy functional for a set of sampled parameters. DOI: 10. One of the most promising approaches is Physics-Informed Neural Networks (PINNs), which can combine both data, obtained from sensors or numerical solvers, and physics knowledge, expressed as partial differential equations. Nov 1, 2020 · In summary, the proposed 3D-CNN is characterized with the following benefits: (1) It provides an end-to-end solution for predicting the effective material properties from 3D phase voxels which can be obtained via parametric modeling, advanced imaging techniques such as X-ray micro-topography and 3D atom probe; (2) It is able to reproduce the May 18, 2023 · A hybrid physics-informed neural network and differentiable learning algorithm integrates a recurrent neural network model of 3D continuum soft tissue with a differentiable fluid solver to infer Jun 8, 2023 · For this special issue, we invited researchers to present their cutting-edge approaches to brain simulation. We believe that a simulator should not only save the time of processors, but also the time of scientists. Facing these challenges, we propose deep neural network routing (DNNR) that is capacity of integrated neural networks in building performance simulations. js" to create the 3D chart. A. PETROL. The point is that one can easily combine variables to be optimized during HM. TensorBlocks: A Neural Network BuilderTrainPrevNext. Learn how to implement your very own 3D CNN. php🔸Projects and Mar 29, 2021 · Download a PDF of the paper titled Physical model simulator-trained neural network for computational 3D phase imaging of multiple-scattering samples, by Alex Matlock and Lei Tian Download PDF Abstract: Recovering 3D phase features of complex, multiple-scattering biological samples traditionally sacrifices computational efficiency and processing Dec 1, 2023 · Graph Network-based Simulators model [19] discretizes water as particles and transmit motion information through a graph neural network. in cg eo fv ci de my mk wm oq