NVIDIA PhysicsNeMo DoMINO-Automotive-Aero NIM

DoMINO is a pre-trained AI model for automotive external aerodynamics. This model has been trained using data from 3200 steady-state RANS simulations of road-cars of varying geometries, at speeds from 20m/s to 50m/s. This pre-trained model works by taking the input from a single STL (Standard Tessellation Language) geometry of a road-car together with a target streamwise velocity and evaluates a solution on the surface of your vehicle as well as the volume surrounding it.

The DoMINO model evaluates solution fields within a computational domain by leveraging geometry representations in STL file format. It encodes global geometry information on a fixed-size grid, defined in the computational domain, through a combination of learnable point convolution kernels, CNNs, and dense networks. Local geometric encoding is extracted, using point convolution kernels, from the global encoding by dynamically constructing local subdomains around sampled points where the solution fields are evaluated. This approach enables the prediction of volume and surface solutions by combining local geometry encoding with basis functions computed for sampled points and their neighboring points.

Key features of DoMINO:

Highly scalable: Predicts high-fidelity flow fields on point clouds sampled on the vehicle surface and in the volume around it.
Efficient geometry representation for better generality: Learns multi-scale global encodings directly from STLs.
Localized learning for improved accuracy: Local geometry encodings are extracted from global representations in subdomains dynamically constructed around each point where the solution is desired.
Basis in traditional numerical methods: Dynamically constructs local computational stencils around sampled points in the domain where the solution is desired. The size of the stencil is user controlled and provides a trade-off between accuracy and speed.

This pre-trained model serves as a demonstration of the potential of foundation models for early stage design evaluation in automotive aerodynamics and is ready for use as a starting point for finetuning to datasets with larger geometric and boundary condition variations.

What is NVIDIA NIM?

NVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to accelerate deployment of generative AI across cloud, data center, and workstations.

Benefits of self-hosted NIMs:

Deploy anywhere and maintain control of generative AI applications and data
Streamline AI application development with industry standard APIs and tools tailored for enterprise environments
Prebuilt containers for the latest generative AI models, offering a diverse range of options and flexibility right out of the gate
Industry-leading latency and throughput for cost-effective scaling
Support for custom models out of the box so models can be trained on domain specific data
Enterprise-grade software with dedicated feature branches, rigorous validation processes, and robust support structures

Getting Started with DoMINO-Automotive-Aero NIM

Please visit the NIM Documentation on how to get started.

Security Vulnerabilities in Open Source Packages

Please review the Security Scanning tab to view the latest security scan results.

For certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.

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Enterprise Support

Get access to knowledge base articles and support cases or submit a ticket.

NVIDIA AI Enterprise Documentation

Visit the NVIDIA AI Enterprise Documentation Hub for release documentation, deployment guides and more.

Governing Terms

The NIM container is governed by the NVIDIA Software License Agreement\nand Product-Specific Terms for AI Products;\nand the use of this model is governed by the NVIDIA Community Model License.

You are responsible for ensuring that your use of NVIDIA AI Foundation Models complies with all applicable laws.

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This model has been trained using data from 3200 steady-state RANS simulations of road-cars of varying geometries, at speeds from 20m/s to 50m/s. This pre-trained model works by taking the input from a single STL (Standard Tessellation Language) geometry of a road-car together with a target streamwise velocity and evaluates a solution on the surface of your vehicle as well as the volume surrounding it.\n\nThe DoMINO model evaluates solution fields within a computational domain by leveraging geometry representations in STL file format. It encodes global geometry information on a fixed-size grid, defined in the computational domain, through a combination of learnable point convolution kernels, CNNs, and dense networks. Local geometric encoding is extracted, using point convolution kernels, from the global encoding by dynamically constructing local subdomains around sampled points where the solution fields are evaluated. This approach enables the prediction of volume and surface solutions by combining local geometry encoding with basis functions computed for sampled points and their neighboring points.\n\nKey features of DoMINO:\n\n- Highly scalable: Predicts high-fidelity flow fields on point clouds sampled on the vehicle surface and in the volume around it.\n- Efficient geometry representation for better generality: Learns multi-scale global encodings directly from STLs.\n- Localized learning for improved accuracy: Local geometry encodings are extracted from global representations in subdomains dynamically constructed around each point where the solution is desired.\n- Basis in traditional numerical methods: Dynamically constructs local computational stencils around sampled points in the domain where the solution is desired. The size of the stencil is user controlled and provides a trade-off between accuracy and speed.\n\nThis pre-trained model serves as a demonstration of the potential of foundation models for early stage design evaluation in automotive aerodynamics and is ready for use as a starting point for finetuning to datasets with larger geometric and boundary condition variations. \n\n\n## What is NVIDIA NIM?\n\nNVIDIA NIM, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed to accelerate deployment of generative AI across cloud, data center, and workstations.\n\nBenefits of self-hosted NIMs:\n\n- Deploy anywhere and maintain control of generative AI applications and data\n- Streamline AI application development with industry standard APIs and tools tailored for enterprise environments\n- Prebuilt containers for the latest generative AI models, offering a diverse range of options and flexibility right out of the gate\n- Industry-leading latency and throughput for cost-effective scaling\n- Support for custom models out of the box so models can be trained on domain specific data\n- Enterprise-grade software with dedicated feature branches, rigorous validation processes, and robust support structures\n\n\n## Getting Started with DoMINO-Automotive-Aero NIM\n\nPlease visit the [NIM Documentation](https://docs.nvidia.com/nim/physicsnemo/domino-automotive-aero/latest/overview.html) on how to get started.\n\n## Security Vulnerabilities in Open Source Packages\n\nPlease review the Security Scanning tab to view the latest security scan results.\n\nFor certain open-source vulnerabilities listed in the scan results, NVIDIA provides a response in the form of a Vulnerability Exploitability eXchange (VEX) document. The VEX information can be reviewed and downloaded from the Security Scanning tab.\n\n## Get Help\n\n### Enterprise Support\n\nGet access to knowledge base articles and support cases or [submit a ticket](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/support/).\n\n### NVIDIA AI Enterprise Documentation\n\nVisit the [NVIDIA AI Enterprise Documentation Hub](https://docs.nvidia.com/ai-enterprise/) for release documentation, deployment guides and more.\n\n## Governing Terms\n\nThe NIM container is governed by the [NVIDIA Software License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-software-license-agreement/)\nand [Product-Specific Terms for AI Products](https://www.nvidia.com/en-us/agreements/enterprise-software/product-specific-terms-for-ai-products/);\nand the use of this model is governed by the [NVIDIA Community Model License](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-community-models-license/).\n\n**You 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","omniverse.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","nvidiaCart","This domain is owned by NVIDIA Corporation which provides information about the company, its products, solutions, industries, news, events, and career opportunities.","nv_co","__uxq412__ses.88c1","AWS ELB application load balancerUsed to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","learn.learn.nvidia.com","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","This cookie is associated with Akamai. It is used to analyse traffic to determine if it is automated traffic generated by bots or a human user.","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","programs.nvidia.com","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","buildapi.ngc.nvidia.com","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","_pf_consent","This cookie name is associated with Command Alkon Incorporated which is used to store user consent status and expiration.","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. It is a temporary cookie used to determine if the browser accepts cookies.","XSRF-TOKEN","This cookie is written to help with site security in preventing Cross-Site Request Forgery attacks.This cookie helps with site security in preventing Cross-Site Request Forgery attacks.","bpazaws52gukakzc__ctrl0_ctl78_list_ctl09_systemcaptcha","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","PHPSESSID","PHP session cookie associated with embedded content from this domain.\n","base.manager.learn.nvidia.com","bm_ss","This cookie is used to help protect websites from automated bot traffic.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","c_code","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","com.adobe.alloy.getTld","This cookie name is associated with the Adobe WebSDK which is used to determine the top-most domain that can accept cookies.","session","This cookie is typically used to maintain an anonymized user session by the server.","SESSls","This cookie is likely to be a session identifier, used to maintain a user's session state across page requests.","BIGipServerx","This cookie name is associated with the BIG-IP product suite from company F5. Usually associated with managing sessions on load balanced servers, to ensure user requests are routed consistently to the correct server. The common root is BIGipServer most commonly followed by a domain name, usually the one that it is hosted on, but not always.","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a one year lifespan and contains no personal information.","default","

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