Democratize AI
for Climate Risks

At Mirai, we're advancing sustainable deep learning solutions to meet complex environmental challenges.
Our strategy enhances the robustness and accuracy of spatio-temporal models, focusing on uncertainty quantification for reliable climate forecasting. We're committed to democratizing deep learning with adaptable foundation models for a range of applications, leveraging extensive climate and remote sensing data for comprehensive environmental insights.

Prioritizing sustainable data practices, we explore cost-effective synthetic data generation methods, emphasizing low carbon footprint techniques. Central to our mission is developing energy-efficient models that reflect our environmental values, underscoring our belief in technology's power to drive impactful, sustainable change.

At the core of our mission is the advancement of robust deep learning methodologies, grounded in physics, to enhance the precision of climate models. Our commitment involves utilizing these sophisticated methods to analyze comprehensive and complex datasets, aiming to diminish uncertainties in climate predictions and to refine the precision and depth of our forecasting models. This endeavor is vital in boosting the dependability of our models, leading to more informed decision-making and the formulation of impactful policies.

Our methodology focuses on developing deep learning models, like GANs and Diffusion models, trained using extensive historical climate data to simulate and forecast a range of climate scenarios, including extreme weather. Integrated with established climate models, these AI tools are key in assessing climate risks and effects, aiding in the development of mitigation strategies and enhancing public awareness and understanding of these challenges.

At Mirai, we are committed to unraveling the complexities of climate change with advanced computation of risk metrics. We concentrate on critical climate risk areas: Event Probability of Occurrence, Severity/Intensity, Exposure, Vulnerability, Resilience, and crafting comprehensive Composite Risk Indices.

Our strategy integrates remote sensing imagery with environmental and geographical data for a holistic view. This approach enables us to offer a detailed understanding of climate risks, crucial for resource-limited regions. We aim to make these critical insights universally accessible, equipping communities everywhere with essential information.