Orbyfy’s Approach: From Climate Risks → Climate Forces → Physical Impacts
At Orbyfy, our city-scale digital twin platform is powered by Physics AI—also known as First-Principles AI—so you get rigorously constrained, explainable simulations rather than opaque, “garbage in, garbage out” predictions.

What Is ‘Black-Box AI’?

• Data-Driven Only: Learns patterns purely from historic data, without respect for underlying physical laws.

• Limited Explainability: Offers little insight into why a model makes a given prediction.

• Risk of Unrealistic Results: Can produce physically impossible scenarios when faced with extremes or data gaps.
  

What Is Physics AI?

• First-Principles Foundation: Neural networks are explicitly constrained by the partial differential equations (PDEs) governing fluid mechanics, structural dynamics, heat transport, and more.

• True Explainability: Every output is backed by the same physics that shape the real world —whether determining flood inundation depths due to river-based floods or wind pressures on buildings from hurricanes.

• Robust Under Extremes: By embedding advection–diffusion–reaction and shallow-water equations directly into the learning process, Physics AI resists “model drift” and remains accurate even under rare, high-impact events.

Why Physics AI Matters for Climate Digital Twins

• Trustworthy Simulations

  • No hidden shortcuts: our models simulate water flow, wind loads, and heat transfer exactly as nature dictates.

• Actionable Insights

  • Explainable risk metrics let engineers, insurers, and city planners understand how and why assets are vulnerable—without needing a PhD in numerical methods, simulation or computational fluid dynamics.

• Versatile Use Cases

  • Hurricane and wind impact simulations

  • Flooding and hydrodynamic simulations

  • Hail and impingement simulations

  • Fire and thermodynamic effect simulations

  • Drought simulations

  • Earthquake and vibrational impacts simulations

  • Erosion and coastal effects simulation