SBIR Phase II: Combining Physics and AI for Enhanced Risk Assessment

The broader impact /commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to improve the ability to quantify risks significantly impacting global insurance and reinsurance markets. By providing accurate and timely risk prediction information months in advance, the proposed technology will help insurers price more precisely, and reduce coverage gaps. It will also assist governments, energy providers, and supply chain operators in better allocating resources. The technology offers a durable competitive advantage through proprietary modeling pipelines, integration of varied datasets, and a stochastic event generation engine that outperforms historical-data-based models. The business model focuses on a SaaS platform with API-based integration into existing risk tools, supported by tiered subscriptions for insurers and enterprise clients. Integrating AI with atmospheric modeling delivers forward-looking risk assessments that adapt to changing climatological patterns - enhancing safety, and economic resilience. This Small Business Innovation Research (SBIR) Phase II project focuses on the development of a next-generation risk prediction platform that combines physics-based models with deep learning and atmospheric foundation models. The research addresses current limitations in predicting destructive events on sub-seasonal to seasonal timescales, which are critical for insurers and planners. The project aims to refine models that generate high-resolution hazard maps and develop a stochastic event simulator capable of producing synthetic scenarios that reflect future atmospheric conditions. Key innovations include multi-peril modeling architectures, probabilistic assessments, and scalable data infrastructure for real-time access. The project outcomes will improve predictive accuracy, uncertainty quantification, and integration into industry-standard risk tools, setting a new benchmark for weather-driven decision support systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2528178 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Mayur Mudigonda | Institution: VAYUH INC, OAKLAND, CA | Award Amount: $1,250,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2528178 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2528178.html