What is Altair PhysicsAI?
Altair PhysicsAI is a groundbreaking technology that integrates artificial intelligence (AI) with physics-based simulation to accelerate engineering workflows. By leveraging machine learning, physics-informed neural networks (PINNs), and surrogate modeling, it accelerates simulation processes, enhances predictive accuracy, and optimizes product designs faster than ever.
For large-scale optimization problems, Altair Physics AI can be deployed in the cloud, allowing for massive parallel computing to handle complex design scenarios efficiently and scalability for enterprise-level engineering challenges.
Key Features
AI-Enhanced Simulation
Altair Physics AI leverages machine learning to reduce simulation runtimes dramatically.
- Train AI models with historical data to generate fast, high-fidelity predictions without running full-scale simulations every time. The AI-driven models go beyond black-box machine learning.
- They incorporate the fundamental laws of physics—ensuring accuracy, consistency, and reliability in every AI-powered prediction.
- Create surrogate models that replace expensive, computationally intense simulations.
Seamless Integration with Altair’s Simulation Ecosystem
Altair Physics AI is built to work effortlessly with Altair HyperWorks, SimLab, and Inspire. Leverage AI-driven physics seamlessly within your existing CAE workflows.
- From fluid dynamics and electromagnetics to structural mechanics and thermal analysis, Altair Physics AI enhances simulations across multiple physics domains—enabling engineers to solve complex problems with unprecedented speed.
AI-Powered Optimization
With AI-driven optimization techniques, engineers can evaluate thousands of design possibilities in real time, identifying the best-performing solutions faster than ever. Altair Physics AI applies advanced multi-objective optimization (MOO) techniques to:
Find the best trade-off solutions between multiple design goals.
Generate Pareto-optimal frontiers, helping engineers visualize and select the best compromise between performance criteria.
Improve decision-making by evaluating multiple constraints in a single optimization run.
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