Collaborative research: caig: transforming understanding and prediction of nearshore processes via AI-powered simultaneous mapping of bathymetry, waves, and currents

The National Science Foundation Office of Integrative Activities awarded Texas A&M University $340,975 on October 1, 2026, under the Geosciences program (CFDA 47.050) to develop physics-informed artificial intelligence tools that estimate nearshore bathymetry, waves, and currents from remote sensing data.

The project creates a physics-informed, remote-sensing-driven and AI-powered modeling (PRAM) system combining deep learning with physical constraints from wave mechanics to produce high-resolution predictions of subsurface ocean conditions using surface observations alone. The system employs physics-informed neural networks and computer vision algorithms to process optical and lidar remote sensing data of the surf zone, enabling estimation of orbital velocities, undertows, and seafloor topography without requiring in-water measurements during hazardous conditions. The resulting open-access system supports federal agencies, coastal engineers, and local planners in predicting beach erosion and designing coastal defenses. The project includes workforce development through hands-on training that prepares students for work at the intersection of coastal science and artificial intelligence.

Performance occurs at Texas A&M University's College Station, Texas facility through September 30, 2029. The award is assistance type Project Grant.

Abstract

Coastal storms erode beaches and increase flood risk, threatening communities, infrastructure, and ecosystems along the nation's shorelines. Predicting how beaches and nearshore waters will change during a storm requires knowing the bathymetry (shape and topography of the seafloor) just offshore and near the shoreline, but this information is notoriously difficult and expensive to collect, particularly during the very storms when IT matters most. This project will develop new artificial intelligence (AI) tools that estimate underwater beach shapes, waves, and currents directly from remotely sensed images, without requiring divers or boats to enter hazardous surf conditions. The resulting open-access system will give federal agencies, coastal engineers, and local planners a more accurate and affordable way to predict beach erosion and design coastal defenses, supporting the national interest in reducing storm damage and protecting vulnerable coastal communities. The project will also create hands-on training opportunities that prepare students to work at the intersection of coastal science and AI, building the workforce needed for AI-driven approaches to environmental and infrastructure challenges. This collaborative project develops physics-informed neural networks (pinns) and AI-powered computer vision algorithms to simultaneously estimate nearshore bathymetry, waves, and currents from optical and lidar remote sensing data of the surf zone. The resulting physics-informed, remote-sensing-driven and AI-powered modeling (pram) system combines deep learning with physical constraints from wave mechanics to produce high-resolution predictions of conditions beneath the ocean surface, including orbital velocities and undertows, using surface observations alone. The research team will field-validate the system across multiple coastal sites to address key science questions concerning wave breaking and roller dynamics, wave skewness and asymmetry in the inner surf zone, and the relationship between hydrodynamic forcing and bathymetric change. By enabling observation of surf-zone processes that are difficult or impossible to measure directly during storms, the project will advance fundamental understanding of nearshore dynamics while establishing a transferable AI methodology applicable to other complex inverse problems in remote sensing and environmental monitoring. 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.- subawards are not planned for this award.