Digital Twin Simulation

Use AI-powered digital twins to simulate and optimize physical systems and processes virtually.

Business impact

• Operational Efficiency — Optimizes processes by simulating scenarios to reduce downtime and waste
• Time to Market — Speeds up development by enabling virtual testing and early flaw detection
• Cost Reduction — Lowers prototyping and rework costs through accurate virtual modeling
• Product Quality — Improves design accuracy and performance via high-fidelity simulations
• Safety Metrics — Enhances safety by predicting hazards and enabling remote operation in risky environments

Data requirements

• Sensor Data (Numeric) — Feeds real-time operational parameters into digital twin models
• 3D Models and BIM (Structured) — Provide structural and spatial data for accurate environment simulation
• Historical Performance Logs (Structured) — Inform predictive models with past system behavior and failures
• Video and Image Data (Image) — Support visual validation and monitoring of physical assets
• Simulation Parameters (Numeric) — Define scenario variables and tunable model inputs for testing

AI methods and techniques

• Predictive AI — Forecasts system behavior and potential failures based on data trends
• Generative AI — Creates synthetic data and scenarios to augment training and testing
• Agentic AI — Enables autonomous decision-making and control within simulations

AI models and model families

GPT-4, Llama 2, Claude, Unreal Engine AI modules, NVIDIA Omniverse AI

Industries

Real-world evidence

15 documented case studies on record.

Companies using this: Bp, Buildroid AI, Duality AI, Emerson, Euro HPC JU, GAMORA, GE Health Care, Integrated Health Projects IHP, Komatsu, MIT, Muffakham Jah College Engineering Technology, Rigetti Computing, Skanska UK, Tata Elxsi, Vaarst.