CINXE.COM

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sessionStorage.removeItem('facets.search.filters'); </script> <div class="col-sm-12 d-none text-right sidebar-toggle-link-wrapper"> <a class="sidebar-toggle sidebar-toggle-link" href="#main-content-end"> Related Resources <i class="fa fa-angle-right" aria-hidden="true"></i> </a> </div> <div class="main-content col-lg-9 col-md-9 mt-0 mb-0"> <div class="post-card--single"> <div class="row"> <div class="col-lg-12 mb-0"> <div class="card--post-attributes"> <span class="category-name content-s"> <a href="https://developer.nvidia.com/blog/category/simulation-modeling-design/" data-wpel-link="internal" target="_blank" rel="follow noopener">Simulation / Modeling / Design</a> </span> <span class="post-rate-widget content-s"></span> <span class="post-lang-switcher"> </span> </div> <h1 class="h--large txt-clr--blck mt-2 mb-0">Spotlight: Advancing Autonomous Operations with AVEVA Dynamic Simulation and NVIDIA Raptor</h1> <div class="post-info"> <div class="post-published-date"> Nov 21, 2024 </div> <div class="post-authors"> By <a href="https://developer.nvidia.com/blog/author/davidasmith/" title="Posts by David A. Smith" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">David A. Smith</a>, <a href="https://developer.nvidia.com/blog/author/srikanthk/" title="Posts by Sri Kodali" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Sri Kodali</a> and <a href="https://developer.nvidia.com/blog/author/jihyuny/" title="Posts by Jihyun Yang" class="author url fn" rel="author follow noopener" data-wpel-link="internal" target="_blank">Jihyun Yang</a> </div> <div class="card--post-attributes-secondary card--post-attributes-header"> <div class="post--rate secondary--attribute"> <div class="wpulike wpulike-heart " ><div class="wp_ulike_general_class wp_ulike_is_not_liked"><button type="button" aria-label="Like Button" data-ulike-id="92360" data-ulike-nonce="4f1d1241a2" data-ulike-type="post" data-ulike-template="wpulike-heart" data-ulike-display-likers="" data-ulike-likers-style="popover" class="wp_ulike_btn wp_ulike_put_text wp_post_btn_92360"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box 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class="for-mail">E</a></li> </ul> </div> <div class="entry-content"> <p>Industrial engineers are turning to AI to build advanced process simulation solutions and accelerate progress toward fully autonomous operations in the energy, power, and chemicals industries.</p> <p>Deep reinforcement learning (DRL) agents, infused with AI, are helping transition traditional industrial automation to industrial autonomy for core control, field, and production processes. This use of <a href="https://blogs.nvidia.com/blog/what-is-agentic-ai/" data-wpel-link="internal" target="_blank" rel="follow noopener">agentic AI</a> for advanced reasoning and iterative planning with human-in-the-loop aims to enable new applications in advanced industrial control, system-level autonomy, and fully autonomous plants.</p> <p>In this post, we explore how AVEVA, a leader in industrial software, integrated its Dynamic Simulation platform with the NVIDIA Raptor DRL engine to maximize industrial plant production, reduce unplanned downtime, and enhance product quality.</p> <div class="wp-block-image"> <figure class="aligncenter size-full-page-width"><a href="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels.png" data-wpel-link="internal" target="_blank" rel="follow noopener"><img decoding="async" width="1024" height="422" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-1024x422.png" alt="A diagram shows increasing levels of industrial autonomy, from 100% manual control (level 0) to 100% human decisions and from there to traditional automation, advanced control, system-level autonomy, and then to full autonomy." class="wp-image-92373" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-1024x422.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-300x124.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-625x258.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-179x74.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-768x317.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-1536x634.png 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-645x266.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-500x206.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-160x66.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-362x149.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels-267x110.png 267w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/autonomy-automation-levels.png 1978w" sizes="(max-width: 1024px) 100vw, 1024px" /></a><figcaption class="wp-element-caption"><em>Figure 1. AVEVA’s scale for industrial automation to industrial autonomy</em></figcaption></figure></div> <h2 id="challenges_and_benefits_of_industrial_automation" class="wp-block-heading">Challenges and benefits of industrial automation<a href="#challenges_and_benefits_of_industrial_automation" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>Conventional advanced control technologies, such as advanced process control or closed-loop real-time optimization are best-fit for repeatable processes. This occurs when plant conditions are relatively constant to optimize energy consumption, emissions, and production. However, industrial plants undergo transient events, such as imposed feed changes or unexpected disturbances, which are difficult to control using either base level or advanced process controls. </p> <p>In these dynamic situations, industrial plant operators switch to manual control to re-stabilize the plant and avoid unwanted and expensive consequences, such as a power plant trip when a power-generating turbine is automatically shut down due to an unexpected event.</p> <p>By achieving greater industrial autonomy, plant operators, managers, and engineers can achieve several benefits:</p> <ul class="wp-block-list"> <li><strong>Enhanced safety</strong>: Reduces worker exposure to hazardous environments.</li> <li><strong>Increased operational efficiency</strong>: Optimizes production and equipment use.</li> <li><strong>Cost reduction</strong>: Optimizes labor and maintenance costs.</li> <li><strong>Improved decision-making</strong>: AI-driven insights for better accuracy and performance.</li> <li><strong>Environmental sustainability</strong>: Optimizes resource usage and reduces emissions.</li> <li><strong>Scalability</strong>: Enables efficient management of large or remote operations.</li> <li><strong>Connected remote operations</strong>: Centralized control of geographically dispersed assets.</li> </ul> <h2 id="building_an_industrial_automation_strategy" class="wp-block-heading">Building an industrial automation strategy<a href="#building_an_industrial_automation_strategy" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>AVEVA and Schneider Electric are bringing their industrial expertise to collaborate with NVIDIA across industrial process, hybrid and compute infrastructure use cases:</p> <ul class="wp-block-list"> <li>Autonomous AI agents trained using physically accurate industrial digital process twins at scale on a high-speed compute architecture.</li> <li>High-fidelity digital twins at scale for industrial 3D environments and unified operation centers to monitor and control industrial operations in real time.</li> <li>Data center reference designs for NVIDIA-accelerated compute clusters that enable operators to support AI workloads on their data center infrastructure, improving the speed of deployment and reliable life-cycle operation.</li> </ul> <h2 id="results_of_aveva_dynamic_simulation_with_nvidia_raptor" class="wp-block-heading">Results of AVEVA Dynamic Simulation with NVIDIA Raptor<a href="#results_of_aveva_dynamic_simulation_with_nvidia_raptor" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>AVEVA Dynamic Simulation, a rigorous first-principles simulator for industrial processes, was tested using an integration with NVIDIA Raptor, a deep reinforcement learning engine. </p> <p>AVEVA Dynamic Simulation is used to build dynamic simulation twins of process plants for operator training simulators, control checkouts, and process design across multiple use cases.</p> <div class="wp-block-image"> <figure class="aligncenter size-full-page-width"><a href="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard.png" data-wpel-link="internal" target="_blank" rel="follow noopener"><img decoding="async" width="1024" height="514" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-1024x514.png" alt="A screenshot shows a process model design with options for thermodynamics, cross-referencing, malfunctions, and training performance monitoring." class="wp-image-92372" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-1024x514.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-300x151.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-625x314.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-179x90.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-768x386.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-1536x771.png 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-645x324.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-500x251.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-160x80.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-362x182.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard-219x110.png 219w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/dynsim-dashboard.png 1633w" sizes="(max-width: 1024px) 100vw, 1024px" /></a><figcaption class="wp-element-caption"><em>Figure 2. AVEVA Dynamic Simulation</em><em> dashboard</em></figcaption></figure></div> <p>Industrial engineers use AVEVA Dynamic Simulation to reduce operating costs, speed time to market for industrial products, enhance worker safety, and optimize plant uptime across a variety of heavy industries.</p> <div class="wp-block-image"> <figure class="aligncenter size-full"><img loading="lazy" decoding="async" width="938" height="378" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial.png" alt="Twelve photos of industrial sites include pipelines, refining, petrochem, gas processing, chemicals, polymers, food and beverage, mining metals and minerals, pulp and paper, cement, power, and green H2." class="wp-image-92371" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial.png 938w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-300x121.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-625x252.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-179x72.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-768x309.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-645x260.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-500x201.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-160x64.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-362x146.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/use-cases-dynsim-industrial-273x110.png 273w" sizes="(max-width: 938px) 100vw, 938px" /><figcaption class="wp-element-caption"><em>Figure 3. Key use cases for AVEVA Dynamic Simulation</em><em> in industrial process simulation</em></figcaption></figure></div> <p>Raptor was trained offline using AVEVA’s dynamic process simulation of industrial process plants. Raptor enables large-scale reinforcement learning training using accelerated computing on-premises or in the cloud. </p> <p>NVIDIA used the Raptor framework internally for chip design using reinforcement learning. Using Raptor, NVIDIA generated arithmetic circuits with a 25% smaller footprint, while maintaining similar performance and accuracy to leading electronic design automation software solutions. Because Raptor supports a wide range of reinforcement learning algorithms, such as proximal policy optimization, it can be put to use to solve industrial problems such as AVEVA Dynamic Simulation.</p> <p>The AVEVA Dynamic Simulation model environment takes an action from the NVIDIA Raptor deep reinforcement learning agent, outputting a new state to Raptor. At each step, the DRL agent gets a reward depending on whether its action resulted in a better or worse state. Raptor learns by maximizing rewards over a series of actions.</p> <p>When trained, the resulting control policy can be deployed in the plant control system:</p> <ul class="wp-block-list"> <li>Minimize production impact of plant upset by stabilizing product quality in a shorter time than a manual plant operator.</li> <li>Improve control of batch processes to deliver consistent and repeatable product quality.</li> <li>Reduce complex startup and shutdown times to maximize production throughput.</li> </ul> <div class="wp-block-image"> <figure class="aligncenter size-full-page-width"><a href="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor.png" data-wpel-link="internal" target="_blank" rel="follow noopener"><img loading="lazy" decoding="async" width="1024" height="405" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-1024x405.png" alt="A three-part flow diagram shows generated actions from AVEVA DynSim and NVIDIA Raptor, control policies, and end process results." class="wp-image-92370" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-1024x405.png 1024w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-300x119.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-625x247.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-179x71.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-768x303.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-1536x607.png 1536w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-645x255.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-500x198.png 500w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-160x63.png 160w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-362x143.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor-278x110.png 278w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/flow-generated-actions-dynsim-raptor.png 1982w" sizes="(max-width: 1024px) 100vw, 1024px" /></a><figcaption class="wp-element-caption"><em>Figure 4. AVEVA Dynamic Simulation</em><em> generates states and rewards, and NVIDIA Raptor DRL agents generate control policies</em></figcaption></figure></div> <p>An example industrial problem is a chemical distillation process to separate heavier propane from lighter methane and ethane. When the incoming feed changes by a larger-than-expected amount, the industrial process is disturbed and conventional controls might not manage levels and product qualities in the correct ranges. Normally, this would require plant operator intervention. </p> <p>A Raptor DRL agent was trained using AVEVA Dynamic Simulation to control heat input to the column, column bottoms level, and reflux drum level. As a result, the DRL agent could stabilize the distillation system twice as fast as a manual plant operator with temperature and product quality remaining in the correct ranges even during large changes in feed, in contrast to conventional industrial base controls.</p> <div class="wp-block-image"> <figure class="aligncenter size-full"><a href="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example.png" data-wpel-link="internal" target="_blank" rel="follow noopener"><img loading="lazy" decoding="async" width="966" height="586" src="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example.png" alt="A diagram shows an example chemical distillation system using AVEVA DynSim and a Raptor DRL agent to generate actions and control policies, automatically managing temperature and product quality control." class="wp-image-92368" srcset="https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example.png 966w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-300x182.png 300w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-625x379.png 625w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-179x109.png 179w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-768x466.png 768w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-645x391.png 645w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-495x300.png 495w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-148x90.png 148w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-362x220.png 362w, https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/distillation-system-example-181x110.png 181w" sizes="(max-width: 966px) 100vw, 966px" /></a><figcaption class="wp-element-caption"><em>Figure 5. Distillation system example </em></figcaption></figure></div> <h2 id="future_of_industrial-process_simulation" class="wp-block-heading">Future of industrial-process simulation<a href="#future_of_industrial-process_simulation" class="heading-anchor-link"><i class="fas fa-link"></i></a></h2> <p>AI and deep reinforcement learning are enabling industrial plant process control to achieve greater levels of autonomy. AVEVA’s accurate process digital twins and the NVIDIA-accelerated computing platform are driving innovation in industrial process simulation from proof of concept to real-world deployments. </p> <p>The next steps for AVEVA are to pilot, productize, and scale AVEVA Dynamic Simulation with Raptor. AVEVA plans to work with some of the world’s biggest energy companies to pilot the solution—an essential first step for customers to gain confidence. A key advantage is that AVEVA Dynamic Simulation with Raptor can be trained, tested, and proven entirely off-line from the actual industrial power plant.</p> <p>After customer pilots have successfully been completed, AVEVA will look to productize and scale up the solution using no-code workflows in AVEVA Connect. This will enable faster training using the NVIDIA-accelerated computing platform with plug-and-play modules for testing and deployment of deep reinforcement learning agents.</p> <p>Learn more about the <a href="https://www.nvidia.com/en-us/on-demand/session/gtcspring23-s51581/" data-wpel-link="internal" target="_blank" rel="follow noopener">AVEVA and NVIDIA collaboration</a> in industrial process simulation. Explore<a href="https://www.aveva.com/en/products/dynamic-simulation/" data-wpel-link="external" target="_blank" rel="follow external noopener"> AVEVA Dynamic Simulation</a> and <a href="https://developer.nvidia.com/blog/designing-arithmetic-circuits-with-deep-reinforcement-learning/" data-wpel-link="internal" target="_blank" rel="follow noopener">NVIDIA Raptor</a>.</p> </div> <div class="block--prospero-assets"> <h2 class="h--smaller txt-clr--blck">Related resources</h2> <ul><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=EXPT63182&ncid=em-even-124008-vt33-23spring#/'>Driving Autonomous Vehicles In the Omniverse (Presented by Ansys, Inc.)</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=SE63005&ncid=em-even-124008-vt33-23spring#/'>Advancing Autonomous Vehicle Simulation with Omniverse and Generative AI</a></li><li>GTC session: <a class='wpel-ignore' target='_blank' href='https://www.nvidia.com/gtc/session-catalog/?tab.catalogallsessionstab=1700692987788001F1cG&search=S62500&ncid=em-even-124008-vt33-23spring#/'>Airborne Modeling and Simulation of Uncrewed Aerial System (UAS) Teams With Omniverse</a></li><li>SDK: <a class='wpel-ignore' target='_blank' href='https://developer.nvidia.com/drive/drive-constellation?ncid=em-nurt-245273-vt33'>DRIVE Constellation</a></li><li>SDK: <a class='wpel-ignore' target='_blank' href='https://developer.nvidia.com/drive/drive-sim?ncid=em-nurt-245273-vt33'>DRIVE Sim</a></li><li>Webinar: <a class='wpel-ignore' target='_blank' href='https://gateway.on24.com/wcc/eh/1407606/lp/3843565/?embedUrl=https://www.nvidia.com/en-us/about-nvidia/webinar-portal/'>Using GPUs to Accelerate HD Mapping and Location-Based Services</a></li></ul> </div> <div class="card--post-attributes-secondary"> <div class="post--comments-count secondary--attribute"> <a href="#entry-content-comments"> <i class="fad fa-comment-alt-lines"></i> Discuss (0) </a> </div> <div class="post--rate secondary--attribute"> <div class="wpulike wpulike-heart " ><div class="wp_ulike_general_class wp_ulike_is_not_liked"><button type="button" aria-label="Like Button" data-ulike-id="92360" data-ulike-nonce="4f1d1241a2" data-ulike-type="post" data-ulike-template="wpulike-heart" data-ulike-display-likers="" data-ulike-likers-style="popover" class="wp_ulike_btn wp_ulike_put_text wp_post_btn_92360"><span><i class="far fa-thumbs-up"></i></span> </button><span class="count-box wp_ulike_counter_up" data-ulike-counter-value="+7"></span> </div></div> <span class="js-like-click like-click"> Like </span> </div> </div> <div class="card--post-attributes-secondary tags"> <div class="caption"> <h2 class="h--smaller txt-clr--blck">Tags</h2> </div> <div class="content-s post-tags--list mt-0"> <a href="https://developer.nvidia.com/blog/category/data-science/" data-wpel-link="internal" target="_blank" rel="follow noopener">Data Science</a> | <a href="https://developer.nvidia.com/blog/category/simulation-modeling-design/" data-wpel-link="internal" target="_blank" rel="follow noopener">Simulation / Modeling / Design</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?industry=Energy" data-wpel-link="internal" target="_blank" rel="follow noopener">Energy</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?products=AI+Foundation+Models" data-wpel-link="internal" target="_blank" rel="follow noopener">AI Foundation Models</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?learning_levels=General+Interest" data-wpel-link="internal" target="_blank" rel="follow noopener">General Interest</a> | <a href="https://developer.nvidia.com/blog/recent-posts/?content_types=Spotlight" data-wpel-link="internal" target="_blank" rel="follow noopener">Spotlight</a> | <a href="https://developer.nvidia.com/blog/tag/featured/" data-wpel-link="internal" target="_blank" rel="follow noopener">featured</a> | <a href="https://developer.nvidia.com/blog/tag/industrial-digitalization-digital-twin/" data-wpel-link="internal" target="_blank" rel="follow noopener">Industrial Digitalization / Digital Twin</a> </div> </div> <div class="post-authors-list"> <div class="entry-content-author"> <div class="caption"> <h2 class="h--smaller txt-clr--blck"> About the Authors </h2> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/david-smith-aveva-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/david-smith-aveva-262x262.jpg 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About David A. Smith </b> <br/> David A. Smith is a chartered mechanical engineer and holds a Ph.D. in fluid mechanics from Imperial College London. The first half of his career in industry was spent mainly with EPC companies, where he led design, development, and commissioning of power plant processes and combustion systems. At AVEVA, Dr. Smith is responsible for AI product innovation with a particular focus on integration of AI technologies with AVEVA’s first principles simulation products for asset management and autonomous operations. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/davidasmith/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by David A. Smith<svg width="16" height="16" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 612"><path d="M305 239c9.4 9.4 9.4 24.6 0 33.9L113 465c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l175-175L79 81c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0L305 239z"/></svg> </a> </div> </div> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/sri-kodali-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/11/sri-kodali-262x262.jpg 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About Sri Kodali </b> <br/> Sri Kodali serves as global head of Developer Relations – Energy and Utilities at NVIDIA, where he leads efforts to integrate AI technologies within the energy and utilities sectors. Drawing on a strong background in digital innovation and data analytics, Sri collaborates with software vendors, system integrators, and end users to drive industry-wide AI adoption, delivering solutions that enhance efficiency and performance. Previously, as general manager at Shell, Sri led digital transformation efforts across trading, supply, manufacturing, retail, and chemicals divisions. His work concentrated on enhancing safety, reliability, productivity, optimization, and strategic process improvements, Sri fostered a culture of innovation and collaboration to bring on business transformation. <div id="author-link"> <a href="https://developer.nvidia.com/blog/author/srikanthk/" rel="author follow noopener" data-wpel-link="internal" target="_blank"> View all posts by Sri Kodali<svg width="16" height="16" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 320 612"><path d="M305 239c9.4 9.4 9.4 24.6 0 33.9L113 465c-9.4 9.4-24.6 9.4-33.9 0s-9.4-24.6 0-33.9l175-175L79 81c-9.4-9.4-9.4-24.6 0-33.9s24.6-9.4 33.9 0L305 239z"/></svg> </a> </div> </div> </div> <div class="media author-info"> <div class="author-media-left media-left"> <img alt='Avatar photo' src='https://developer-blogs.nvidia.com/wp-content/uploads/2024/10/Jihyun-Yang-131x131.jpg' srcset='https://developer-blogs.nvidia.com/wp-content/uploads/2024/10/Jihyun-Yang-262x262.jpg 2x' class='avatar avatar-131 photo' height='131' width='131' loading='lazy' decoding='async'/> </div> <div class="author-media-body media-body"> <b> About Jihyun Yang </b> <br/> Jihyun Yang is a solutions architect at NVIDIA with expertise in the field of geophysical imaging/inversion, optimization, and high-performance computing (HPC). Prior to NVIDIA, Jihyun gained experience in applied machine learning at BP and Los Alamos National Laboratory. 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7004|FreeTAB 7800|FreeTAB 2096|FreeTAB 7.5|FreeTAB 1014|FreeTAB 1001 |FreeTAB 8001|FreeTAB 9706|FreeTAB 9702|FreeTAB 7003|FreeTAB 7002|FreeTAB 1002|FreeTAB 7801|FreeTAB 1331|FreeTAB 1004|FreeTAB 8002|FreeTAB 8014|FreeTAB 9704|FreeTAB 1003",VoninoTablet:"\\b(Argus[ _]?S|Diamond[ _]?79HD|Emerald[ _]?78E|Luna[ _]?70C|Onyx[ _]?S|Onyx[ _]?Z|Orin[ _]?HD|Orin[ _]?S|Otis[ _]?S|SpeedStar[ _]?S|Magnet[ _]?M9|Primus[ _]?94[ _]?3G|Primus[ _]?94HD|Primus[ _]?QS|Android.*\\bQ8\\b|Sirius[ _]?EVO[ _]?QS|Sirius[ _]?QS|Spirit[ _]?S)\\b",ECSTablet:"V07OT2|TM105A|S10OT1|TR10CS1",StorexTablet:"eZee[_']?(Tab|Go)[0-9]+|TabLC7|Looney Tunes Tab",VodafoneTablet:"SmartTab([ ]+)?[0-9]+|SmartTabII10|SmartTabII7|VF-1497|VFD 1400",EssentielBTablet:"Smart[ ']?TAB[ ]+?[0-9]+|Family[ ']?TAB2",RossMoorTablet:"RM-790|RM-997|RMD-878G|RMD-974R|RMT-705A|RMT-701|RME-601|RMT-501|RMT-711",iMobileTablet:"i-mobile i-note",TolinoTablet:"tolino tab [0-9.]+|tolino shine",AudioSonicTablet:"\\bC-22Q|T7-QC|T-17B|T-17P\\b",AMPETablet:"Android.* A78 ",SkkTablet:"Android.* (SKYPAD|PHOENIX|CYCLOPS)",TecnoTablet:"TECNO P9|TECNO DP8D",JXDTablet:"Android.* \\b(F3000|A3300|JXD5000|JXD3000|JXD2000|JXD300B|JXD300|S5800|S7800|S602b|S5110b|S7300|S5300|S602|S603|S5100|S5110|S601|S7100a|P3000F|P3000s|P101|P200s|P1000m|P200m|P9100|P1000s|S6600b|S908|P1000|P300|S18|S6600|S9100)\\b",iJoyTablet:"Tablet (Spirit 7|Essentia|Galatea|Fusion|Onix 7|Landa|Titan|Scooby|Deox|Stella|Themis|Argon|Unique 7|Sygnus|Hexen|Finity 7|Cream|Cream X2|Jade|Neon 7|Neron 7|Kandy|Scape|Saphyr 7|Rebel|Biox|Rebel|Rebel 8GB|Myst|Draco 7|Myst|Tab7-004|Myst|Tadeo Jones|Tablet Boing|Arrow|Draco Dual Cam|Aurix|Mint|Amity|Revolution|Finity 9|Neon 9|T9w|Amity 4GB Dual Cam|Stone 4GB|Stone 8GB|Andromeda|Silken|X2|Andromeda II|Halley|Flame|Saphyr 9,7|Touch 8|Planet|Triton|Unique 10|Hexen 10|Memphis 4GB|Memphis 8GB|Onix 10)",FX2Tablet:"FX2 PAD7|FX2 PAD10",XoroTablet:"KidsPAD 701|PAD[ ]?712|PAD[ ]?714|PAD[ ]?716|PAD[ ]?717|PAD[ ]?718|PAD[ ]?720|PAD[ ]?721|PAD[ ]?722|PAD[ ]?790|PAD[ ]?792|PAD[ ]?900|PAD[ ]?9715D|PAD[ ]?9716DR|PAD[ ]?9718DR|PAD[ ]?9719QR|PAD[ ]?9720QR|TelePAD1030|Telepad1032|TelePAD730|TelePAD731|TelePAD732|TelePAD735Q|TelePAD830|TelePAD9730|TelePAD795|MegaPAD 1331|MegaPAD 1851|MegaPAD 2151",ViewsonicTablet:"ViewPad 10pi|ViewPad 10e|ViewPad 10s|ViewPad E72|ViewPad7|ViewPad E100|ViewPad 7e|ViewSonic VB733|VB100a",VerizonTablet:"QTAQZ3|QTAIR7|QTAQTZ3|QTASUN1|QTASUN2|QTAXIA1",OdysTablet:"LOOX|XENO10|ODYS[ -](Space|EVO|Xpress|NOON)|\\bXELIO\\b|Xelio10Pro|XELIO7PHONETAB|XELIO10EXTREME|XELIOPT2|NEO_QUAD10",CaptivaTablet:"CAPTIVA PAD",IconbitTablet:"NetTAB|NT-3702|NT-3702S|NT-3702S|NT-3603P|NT-3603P|NT-0704S|NT-0704S|NT-3805C|NT-3805C|NT-0806C|NT-0806C|NT-0909T|NT-0909T|NT-0907S|NT-0907S|NT-0902S|NT-0902S",TeclastTablet:"T98 4G|\\bP80\\b|\\bX90HD\\b|X98 Air|X98 Air 3G|\\bX89\\b|P80 3G|\\bX80h\\b|P98 Air|\\bX89HD\\b|P98 3G|\\bP90HD\\b|P89 3G|X98 3G|\\bP70h\\b|P79HD 3G|G18d 3G|\\bP79HD\\b|\\bP89s\\b|\\bA88\\b|\\bP10HD\\b|\\bP19HD\\b|G18 3G|\\bP78HD\\b|\\bA78\\b|\\bP75\\b|G17s 3G|G17h 3G|\\bP85t\\b|\\bP90\\b|\\bP11\\b|\\bP98t\\b|\\bP98HD\\b|\\bG18d\\b|\\bP85s\\b|\\bP11HD\\b|\\bP88s\\b|\\bA80HD\\b|\\bA80se\\b|\\bA10h\\b|\\bP89\\b|\\bP78s\\b|\\bG18\\b|\\bP85\\b|\\bA70h\\b|\\bA70\\b|\\bG17\\b|\\bP18\\b|\\bA80s\\b|\\bA11s\\b|\\bP88HD\\b|\\bA80h\\b|\\bP76s\\b|\\bP76h\\b|\\bP98\\b|\\bA10HD\\b|\\bP78\\b|\\bP88\\b|\\bA11\\b|\\bA10t\\b|\\bP76a\\b|\\bP76t\\b|\\bP76e\\b|\\bP85HD\\b|\\bP85a\\b|\\bP86\\b|\\bP75HD\\b|\\bP76v\\b|\\bA12\\b|\\bP75a\\b|\\bA15\\b|\\bP76Ti\\b|\\bP81HD\\b|\\bA10\\b|\\bT760VE\\b|\\bT720HD\\b|\\bP76\\b|\\bP73\\b|\\bP71\\b|\\bP72\\b|\\bT720SE\\b|\\bC520Ti\\b|\\bT760\\b|\\bT720VE\\b|T720-3GE|T720-WiFi",OndaTablet:"\\b(V975i|Vi30|VX530|V701|Vi60|V701s|Vi50|V801s|V719|Vx610w|VX610W|V819i|Vi10|VX580W|Vi10|V711s|V813|V811|V820w|V820|Vi20|V711|VI30W|V712|V891w|V972|V819w|V820w|Vi60|V820w|V711|V813s|V801|V819|V975s|V801|V819|V819|V818|V811|V712|V975m|V101w|V961w|V812|V818|V971|V971s|V919|V989|V116w|V102w|V973|Vi40)\\b[\\s]+|V10 \\b4G\\b",JaytechTablet:"TPC-PA762",BlaupunktTablet:"Endeavour 800NG|Endeavour 1010",DigmaTablet:"\\b(iDx10|iDx9|iDx8|iDx7|iDxD7|iDxD8|iDsQ8|iDsQ7|iDsQ8|iDsD10|iDnD7|3TS804H|iDsQ11|iDj7|iDs10)\\b",EvolioTablet:"ARIA_Mini_wifi|Aria[ _]Mini|Evolio X10|Evolio X7|Evolio X8|\\bEvotab\\b|\\bNeura\\b",LavaTablet:"QPAD E704|\\bIvoryS\\b|E-TAB IVORY|\\bE-TAB\\b",AocTablet:"MW0811|MW0812|MW0922|MTK8382|MW1031|MW0831|MW0821|MW0931|MW0712",MpmanTablet:"MP11 OCTA|MP10 OCTA|MPQC1114|MPQC1004|MPQC994|MPQC974|MPQC973|MPQC804|MPQC784|MPQC780|\\bMPG7\\b|MPDCG75|MPDCG71|MPDC1006|MP101DC|MPDC9000|MPDC905|MPDC706HD|MPDC706|MPDC705|MPDC110|MPDC100|MPDC99|MPDC97|MPDC88|MPDC8|MPDC77|MP709|MID701|MID711|MID170|MPDC703|MPQC1010",CelkonTablet:"CT695|CT888|CT[\\s]?910|CT7 Tab|CT9 Tab|CT3 Tab|CT2 Tab|CT1 Tab|C820|C720|\\bCT-1\\b",WolderTablet:"miTab \\b(DIAMOND|SPACE|BROOKLYN|NEO|FLY|MANHATTAN|FUNK|EVOLUTION|SKY|GOCAR|IRON|GENIUS|POP|MINT|EPSILON|BROADWAY|JUMP|HOP|LEGEND|NEW AGE|LINE|ADVANCE|FEEL|FOLLOW|LIKE|LINK|LIVE|THINK|FREEDOM|CHICAGO|CLEVELAND|BALTIMORE-GH|IOWA|BOSTON|SEATTLE|PHOENIX|DALLAS|IN 101|MasterChef)\\b",MediacomTablet:"M-MPI10C3G|M-SP10EG|M-SP10EGP|M-SP10HXAH|M-SP7HXAH|M-SP10HXBH|M-SP8HXAH|M-SP8MXA",MiTablet:"\\bMI PAD\\b|\\bHM NOTE 1W\\b",NibiruTablet:"Nibiru M1|Nibiru Jupiter One",NexoTablet:"NEXO NOVA|NEXO 10|NEXO AVIO|NEXO FREE|NEXO GO|NEXO EVO|NEXO 3G|NEXO SMART|NEXO KIDDO|NEXO MOBI",LeaderTablet:"TBLT10Q|TBLT10I|TBL-10WDKB|TBL-10WDKBO2013|TBL-W230V2|TBL-W450|TBL-W500|SV572|TBLT7I|TBA-AC7-8G|TBLT79|TBL-8W16|TBL-10W32|TBL-10WKB|TBL-W100",UbislateTablet:"UbiSlate[\\s]?7C",PocketBookTablet:"Pocketbook",KocasoTablet:"\\b(TB-1207)\\b",HisenseTablet:"\\b(F5281|E2371)\\b",Hudl:"Hudl HT7S3|Hudl 2",TelstraTablet:"T-Hub2",GenericTablet:"Android.*\\b97D\\b|Tablet(?!.*PC)|BNTV250A|MID-WCDMA|LogicPD Zoom2|\\bA7EB\\b|CatNova8|A1_07|CT704|CT1002|\\bM721\\b|rk30sdk|\\bEVOTAB\\b|M758A|ET904|ALUMIUM10|Smartfren Tab|Endeavour 1010|Tablet-PC-4|Tagi Tab|\\bM6pro\\b|CT1020W|arc 10HD|\\bTP750\\b|\\bQTAQZ3\\b|WVT101|TM1088|KT107"},oss:{AndroidOS:"Android",BlackBerryOS:"blackberry|\\bBB10\\b|rim tablet os",PalmOS:"PalmOS|avantgo|blazer|elaine|hiptop|palm|plucker|xiino",SymbianOS:"Symbian|SymbOS|Series60|Series40|SYB-[0-9]+|\\bS60\\b",WindowsMobileOS:"Windows CE.*(PPC|Smartphone|Mobile|[0-9]{3}x[0-9]{3})|Windows Mobile|Windows Phone [0-9.]+|WCE;",WindowsPhoneOS:"Windows Phone 10.0|Windows Phone 8.1|Windows Phone 8.0|Windows Phone OS|XBLWP7|ZuneWP7|Windows NT 6.[23]; ARM;",iOS:"\\biPhone.*Mobile|\\biPod|\\biPad|AppleCoreMedia",iPadOS:"CPU OS 13",SailfishOS:"Sailfish",MeeGoOS:"MeeGo",MaemoOS:"Maemo",JavaOS:"J2ME/|\\bMIDP\\b|\\bCLDC\\b",webOS:"webOS|hpwOS",badaOS:"\\bBada\\b",BREWOS:"BREW"},uas:{Chrome:"\\bCrMo\\b|CriOS|Android.*Chrome/[.0-9]* (Mobile)?",Dolfin:"\\bDolfin\\b",Opera:"Opera.*Mini|Opera.*Mobi|Android.*Opera|Mobile.*OPR/[0-9.]+$|Coast/[0-9.]+",Skyfire:"Skyfire",Edge:"\\bEdgiOS\\b|Mobile Safari/[.0-9]* Edge",IE:"IEMobile|MSIEMobile",Firefox:"fennec|firefox.*maemo|(Mobile|Tablet).*Firefox|Firefox.*Mobile|FxiOS",Bolt:"bolt",TeaShark:"teashark",Blazer:"Blazer",Safari:"Version((?!\\bEdgiOS\\b).)*Mobile.*Safari|Safari.*Mobile|MobileSafari",WeChat:"\\bMicroMessenger\\b",UCBrowser:"UC.*Browser|UCWEB",baiduboxapp:"baiduboxapp",baidubrowser:"baidubrowser",DiigoBrowser:"DiigoBrowser",Mercury:"\\bMercury\\b",ObigoBrowser:"Obigo",NetFront:"NF-Browser",GenericBrowser:"NokiaBrowser|OviBrowser|OneBrowser|TwonkyBeamBrowser|SEMC.*Browser|FlyFlow|Minimo|NetFront|Novarra-Vision|MQQBrowser|MicroMessenger",PaleMoon:"Android.*PaleMoon|Mobile.*PaleMoon"},props:{Mobile:"Mobile/[VER]",Build:"Build/[VER]",Version:"Version/[VER]",VendorID:"VendorID/[VER]",iPad:"iPad.*CPU[a-z ]+[VER]",iPhone:"iPhone.*CPU[a-z ]+[VER]",iPod:"iPod.*CPU[a-z ]+[VER]",Kindle:"Kindle/[VER]",Chrome:["Chrome/[VER]","CriOS/[VER]","CrMo/[VER]"],Coast:["Coast/[VER]"],Dolfin:"Dolfin/[VER]",Firefox:["Firefox/[VER]","FxiOS/[VER]"],Fennec:"Fennec/[VER]",Edge:"Edge/[VER]",IE:["IEMobile/[VER];","IEMobile [VER]","MSIE [VER];","Trident/[0-9.]+;.*rv:[VER]"],NetFront:"NetFront/[VER]",NokiaBrowser:"NokiaBrowser/[VER]",Opera:[" OPR/[VER]","Opera Mini/[VER]","Version/[VER]"],"Opera Mini":"Opera Mini/[VER]","Opera Mobi":"Version/[VER]",UCBrowser:["UCWEB[VER]","UC.*Browser/[VER]"],MQQBrowser:"MQQBrowser/[VER]",MicroMessenger:"MicroMessenger/[VER]",baiduboxapp:"baiduboxapp/[VER]",baidubrowser:"baidubrowser/[VER]",SamsungBrowser:"SamsungBrowser/[VER]",Iron:"Iron/[VER]",Safari:["Version/[VER]","Safari/[VER]"],Skyfire:"Skyfire/[VER]",Tizen:"Tizen/[VER]",Webkit:"webkit[ /][VER]",PaleMoon:"PaleMoon/[VER]",SailfishBrowser:"SailfishBrowser/[VER]",Gecko:"Gecko/[VER]",Trident:"Trident/[VER]",Presto:"Presto/[VER]",Goanna:"Goanna/[VER]",iOS:" \\bi?OS\\b [VER][ ;]{1}",Android:"Android [VER]",Sailfish:"Sailfish [VER]",BlackBerry:["BlackBerry[\\w]+/[VER]","BlackBerry.*Version/[VER]","Version/[VER]"],BREW:"BREW [VER]",Java:"Java/[VER]","Windows Phone OS":["Windows Phone OS [VER]","Windows Phone [VER]"],"Windows Phone":"Windows Phone [VER]","Windows CE":"Windows CE/[VER]","Windows NT":"Windows NT [VER]",Symbian:["SymbianOS/[VER]","Symbian/[VER]"],webOS:["webOS/[VER]","hpwOS/[VER];"]},utils:{Bot:"Googlebot|facebookexternalhit|Google-AMPHTML|s~amp-validator|AdsBot-Google|Google Keyword Suggestion|Facebot|YandexBot|YandexMobileBot|bingbot|ia_archiver|AhrefsBot|Ezooms|GSLFbot|WBSearchBot|Twitterbot|TweetmemeBot|Twikle|PaperLiBot|Wotbox|UnwindFetchor|Exabot|MJ12bot|YandexImages|TurnitinBot|Pingdom|contentkingapp|AspiegelBot",MobileBot:"Googlebot-Mobile|AdsBot-Google-Mobile|YahooSeeker/M1A1-R2D2",DesktopMode:"WPDesktop",TV:"SonyDTV|HbbTV",WebKit:"(webkit)[ /]([\\w.]+)",Console:"\\b(Nintendo|Nintendo WiiU|Nintendo 3DS|Nintendo Switch|PLAYSTATION|Xbox)\\b",Watch:"SM-V700"}},g.detectMobileBrowsers={fullPattern:/(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i, shortPattern:/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i,tabletPattern:/android|ipad|playbook|silk/i};var h,i=Object.prototype.hasOwnProperty;return g.FALLBACK_PHONE="UnknownPhone",g.FALLBACK_TABLET="UnknownTablet",g.FALLBACK_MOBILE="UnknownMobile",h="isArray"in Array?Array.isArray:function(a){return"[object Array]"===Object.prototype.toString.call(a)},function(){var a,b,c,e,f,j,k=g.mobileDetectRules;for(a in k.props)if(i.call(k.props,a)){for(b=k.props[a],h(b)||(b=[b]),f=b.length,e=0;e<f;++e)c=b[e],j=c.indexOf("[VER]"),j>=0&&(c=c.substring(0,j)+"([\\w._\\+]+)"+c.substring(j+5)),b[e]=new RegExp(c,"i");k.props[a]=b}d(k.oss),d(k.phones),d(k.tablets),d(k.uas),d(k.utils),k.oss0={WindowsPhoneOS:k.oss.WindowsPhoneOS,WindowsMobileOS:k.oss.WindowsMobileOS}}(),g.findMatch=function(a,b){for(var c in a)if(i.call(a,c)&&a[c].test(b))return c;return null},g.findMatches=function(a,b){var c=[];for(var d in a)i.call(a,d)&&a[d].test(b)&&c.push(d);return c},g.getVersionStr=function(a,b){var c,d,e,f,h=g.mobileDetectRules.props;if(i.call(h,a))for(c=h[a],e=c.length,d=0;d<e;++d)if(f=c[d].exec(b),null!==f)return f[1];return null},g.getVersion=function(a,b){var c=g.getVersionStr(a,b);return c?g.prepareVersionNo(c):NaN},g.prepareVersionNo=function(a){var b;return b=a.split(/[a-z._ \/\-]/i),1===b.length&&(a=b[0]),b.length>1&&(a=b[0]+".",b.shift(),a+=b.join("")),Number(a)},g.isMobileFallback=function(a){return g.detectMobileBrowsers.fullPattern.test(a)||g.detectMobileBrowsers.shortPattern.test(a.substr(0,4))},g.isTabletFallback=function(a){return g.detectMobileBrowsers.tabletPattern.test(a)},g.prepareDetectionCache=function(a,c,d){if(a.mobile===b){var 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