About this Opportunity:

Flight sciences teams rely on aerodynamic databases for performance, stability and control, loads, and mission analysis. But conventional CFD and testing can require thousands of runs, limiting design-space coverage and slowing critical decisions.

In this webinar, see how Physics AI is used to predict aerodynamic fields, forces, moments, and coefficients in seconds. We’ll show how teams can build and expand AeroDBs faster, explore more of the flight envelope, and deliver high-fidelity data earlier for design, controls, and mission workflows.

What You'll Learn:

• How an AI-powered aerodynamic database is built: geometry and operating-condition parameterization, data-generation strategy, and model-training approach
• How model accuracy is evaluated: validation against held-out CFD cases, field predictions, and integrated quantities such as forces, moments, and coefficients
• How Physics AI expands design-space coverage: rapidly predict performance for new configurations and conditions without running a full CFD campaign for every case

What This Enables

• Rapid aerodynamic database generation: Populate large design and operating envelopes in a fraction of the time required by conventional workflows
• Flight dynamics and mission analysis: Supply aerodynamic coefficients and performance predictions across a broad range of operating conditions
• Multidisciplinary design optimization: Embed fast aerodynamic inference directly inside optimization loops to assess thousands of candidates

Who Should Attend:

• Flight sciences, controls, loads, performance, and vehicle-design teams that depend on aerodynamic data across wide operating envelopes
• Aerospace, defense, automotive, and mobility engineering teams building or maintaining aerodynamic databases
• CFD and aerodynamic engineers looking to reduce simulation turnaround time while preserving high-fidelity insight
• AI/ML practitioners working on geometry-conditioned learning, field prediction, PDE surrogates, or physics-informed systems