RFS: Machine learning to simulate the physical world

Current Market Landscape

• Software tools utilizing physics-based models are currently deployed for high-complexity tasks, including weather prediction, fluid dynamics, rocket design, and drug discovery (specifically molecule interaction prediction).
• These existing physics-based solutions face significant computational bottlenecks, requiring supercomputers or extended processing times (days to weeks) due to the need to solve large-scale mathematical models.

Technological Shift to AI

• AI models function as general function approximators capable of solving these specific physics problems with drastically improved efficiency.
• AI-driven predictions can be executed in seconds or minutes on standard hardware, eliminating the dependency on supercomputers.

Strategic Opportunity

• The initiative targets founders aiming to enter the market by leveraging AI to address previously unaddressable markets.
• The core value proposition is the reduction of prediction latency from days/weeks to seconds, enabling new use cases that were previously computationally infeasible.