RESEARCH

FDL 2022

This year we tackled challenges in the areas of: Energy Futures, Earth Science, Disaster Response, Climate Adaptation, Astrobiology, Lunar Exploration and Heliophysics.

FDL.AI’s ability to attract the best researchers from around the world is part of its success formula - but not all. Over the years, we’ve evolved numerous process innovations that allow FDL.AI research teams to consistently deliver world-class outcomes for our Federal stakeholders at NASA, DOE, and USGS over a very accelerated time-scale.

Challenges Videos

Navigate to research areas

  • Can we use AI to emulate Amanzi-ATS simulations to deliver rapid and accurate results to practitioners and enable decision-making on future climate scenarios without a supercomputer?

  • Can physics-informed machine learning models be applied to geomechanical and geophysical datasets to advance innovations in forecasting induced seismicity rates in potential CO2 sequestration sites?

  • Geomechanics for CO2 Sequestration Final Showcase

  • Seismic Analysis for Induced Forecasts.
    Utilizing numerical computing best practices, efficient optimizers, and dimensionality reduction strategies, this challenge successfully reduced model train time from 22 hours to 3 minutes, enabling near real-time forecasts.
    Authors: Giuseppe Castiglione, Alexandre Chen, Akshay Suresh, Han Xiao, Kayla Kroll, Christopher Sherman, Constantin Weisser.

  • Can AI/ML improve the detection of radioactive materials in urban environments, improving sensitivity, accuracy and response time, enhancing national security, environmental remediation and public safety?

  • Can we use ML-enhanced tools to prevent fires from starting, or new fires from combining to create mega-fires?

  • Can AI/ML improve the detection of radioactive materials in urban environments, improving sensitivity, accuracy and response time, enhancing national security, environmental remediation and public safety?

  • Can we use AI tools such as NLP to harvest and explore both historical and emerging research and determine candidate processes that might scale-up to useful levels? Can AI help us discover new ideas and perhaps rank the most promising?

  • The H2 Discovery Engine Final Showcase

  • Solving climate change one atom of hydrogen at a time.
    In this work the team developed the H2 Golden Retriever (H2GR) system for H2 knowledge discovery and representation accessible via an interface tailored for improved decision-making. The tool utilizes a combination of NLP & KG to organize information and recommend promising papers.
    Authors: Paul Seurin, Joseph Wiggins,Olusola Olabanjo, Rozhin Yasei, Lorien Pratt, Loveneesh Rana, Gregory Renard

  • Can we teach AI the relationships within a given CSP control scheme (how CO2 flow, temperature and pressure varies with changes in ambient temp and load demand), to optimize electricity production? 

  • Can ML be used to intercalibrate multi-viewpoint observations of our star?

  • Can we use ML to distinguish space-weather related perturbations in geomagnetic and ionospheric data from those related to seismic or pre-earthquake signals?

  • Can we understand how ML SSL methods should be adapted to SAR data and how to devise SSL pretraining tasks that effectively exploit the particularities of SAR for earth science? 

  • Can we use NLP to develop more effective discoveries by embedding modern language models with the “scientific expertise” to suggest potentially useful connections for researchers?

  • Can we use AI to evaluate the biotic / abiotic divide, with a view to giving robot explorers a wider understanding of life, as “we don’t know it”?

  • Can we use ML to identify the faint and transient signatures of features such as fresh craters, landslides, and tectonic activity on the whole Moon?