Reduce the time from 3D asset ingestion to simulation-ready outputs by unifying spatial data management, rendering, and format conversion in a single, connected workflow. Your teams can move faster from design to validated simulation without rebuilding data pipelines for each project.
Overview
This Guidance demonstrates how to accelerate Physical AI development by centralizing 3D assets, operational data, and enterprise systems into a unified, simulation-ready environment on AWS. It ingests diverse sources and transforms them into standard formats such as USD, URDF, and MJCF, while an Amazon Neptune knowledge graph maintains a digital thread for traceability across products and simulation outputs. Teams run simulations at scale across engineering, robotics, and synthetic-data workloads, apply ML-assisted optimization with Amazon SageMaker and Amazon Bedrock AgentCore, and securely discover and reuse assets from a central catalog. This reduces data silos, shortens development cycles, and speeds time-to-deployment across manufacturing, robotics, and autonomous systems.
Benefits
Accelerate physical AI development cycles
Scale any simulation workload on demand
Run high-fidelity engineering simulations, robotics workloads, and ML surrogate training across flexible compute options—from HPC clusters to containerized environments—without managing fixed infrastructure. You pay only for the compute you use, letting you run more simulation iterations and design sweeps within your existing budget.
Close the simulation-to-deployment loop
Connect simulation outputs, trained surrogate models, and validated parameters back to a shared digital thread, giving your teams full traceability from virtual testing to physical asset deployment. AI agents built on Amazon Bedrock AgentCore can autonomously monitor and optimize simulation runs, continuously improving model fidelity with each iteration.
How it works
This architecture diagram illustrates how to build and operate Physical AI Simulation Platform on AWS. It shows the key components and their interactions.
Download the architecture diagramData and simulation architecture
Step 1
Ingest 3D data, assets, scenes, and metadata using AWS DataSync for continuous replication, or upload directly via CLI/SDK to Amazon S3. Operational data from programmable logic controllers (PLCs) and IoT devices flows through AWS IoT Core. AWS Glue connectors extract and transform data from PLM, ERP, and WMS systems.
SDMA stores, manages, and connects spatial data as a single source of truth. Amazon S3 holds raw 3D assets and indexes metadata for search. AWS Deadline Cloud runs transformations, format conversions, and rendering. A connector framework (AWS Lambda, Amazon API Gateway) enables transformations and data exchange.
SDMA connectors pull operational data and asset metadata into the digital thread. Amazon Neptune builds a knowledge graph capturing hierarchy and relationships across products, operational context, and simulation-ready formats. Amazon Bedrock answers natural-language queries for discovery and traceability. For enterprise integrations, the AWS Digital Thread solution provides connectivity.
SDMA stores transformed assets back in Amazon S3 in simulation-ready formats. The format depends on the simulation platform and use case. Assets then reach consumers and downstream simulation environments, ready for distribution.
The web data portal and SDK distribute and reuse stored assets with secure authentication and access control. Amazon CloudFront delivers assets globally at low latency. Discover and select assets from a central catalog based on your application needs, increasing reuse and removing data silos.
Amazon EC2 powers real-time simulation environments, AWS Batch and AWS Deadline Cloud handle offline workloads, and Amazon EKS runs containerized platforms at scale, also supporting synthetic data generation. Engineering Development Hub (EDH) provides HPC scheduling and licensing; Research and Engineering Studio (RES) delivers NICE DCV virtual desktops for development.
ML enhances simulation two ways. Amazon SageMaker trains surrogate models that cut simulation time and improve compute efficiency. Amazon Bedrock AgentCore runs AI agents that monitor and control simulations, optimizing inputs, evaluating outputs, and searching for optimal scenarios. Amazon Strands agents orchestrate the ML-simulation loop, automating coordination and validation pipelines.
Amazon ECR stores packaged ML simulation models with version management for deployment. SDMA stores simulation applications and outputs in Amazon S3. This registry enables progressive deployment, rollback, and distribution of validated artifacts. Export validated models as Functional Mock-up Unit (FMU) packages for interoperability with external engineering tools.
Write simulation outputs, validated parameters, trained surrogates, and optimization results, back to the AWS Digital Thread for full provenance between configurations and outcomes. The enriched knowledge graph improves each iteration's fidelity, tracking surrogate model versions alongside the high-fidelity data that produced them.
Simulation workload mapping
Step 1
Run tightly-coupled Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and multi-physics at full numerical fidelity. AWS ParallelCluster manages Slurm-based HPC clusters with Elastic Fabric Adapter (EFA) for Message Passing Interface (MPI) workloads. AWS Batch runs independent parametric sweeps.
Model embodiment, motion, contact, and sensor realism with physics-based rendering. Amazon EC2 G6e GPU instances provide high compute. AWS Batch orchestrates headless jobs at scale. Amazon EKS runs persistent containerized workflows combining simulation, ROS 2, and training.
Replace expensive solvers with learned physics approximations for fast inference. Amazon SageMaker provides managed training and real-time inference endpoints. AWS Batch orchestrates training sweeps across GPU clusters.
Apply domain randomization and generative models for long-tail coverage. AWS Batch orchestrates rendering jobs on GPU instances. Amazon SageMaker HyperPod trains world foundation models at scale.
Generates validation evidence. Functional Mock-up Unit (FMU) packages physics for commissioning. Reduced-Order Models (ROM) enable real-time edge inference. ML surrogates accelerate design sweeps at microsecond latency.
Deploy trained robot policies for navigation, manipulation, and locomotion at the edge. Physical AI (PAI) foundation models, vision-language-action (VLA) or world models fine-tuned on simulation data, drive autonomous decision-making.