- Intelligent Systems Division
- Ames Exploration Technology Directorate   - Ames Exploration Technology
  - Entry Systems and Technology
  - Human Systems Integration
  - Intelligent Systems
  - NASA Advanced Supercomputing
  - Ames Research Center
- Autonomous Systems and Robotics   - ASR Overview
  - ASR Research Groups
  - Advanced Control and Evolvable Systems
  - Deployable Autonomy Technologies
  - Intelligent Robotics
  - Planning and Scheduling
- Collaborative and Assistant Systems   - CAS Overview
  - CAS Research Groups
  - Data Repository Systems
  - Decision Support Systems
  - Enterprise Information Management
  - Ground and Flight Data Systems
  - Information Integration
- Discovery and Systems Health   - DaSH Overview
  - DaSH Research Groups
  - Applied Physics
  - Computational Materials
  - Data Sciences
  - Diagnostics and Prognostics
  - ISHM Technology Insertion Projects
  - Prognostics Center of Excellence
  - Quantum Artificial Intelligence Laboratory
- Robust Software Engineering   - RSE Overview
  - Core Avionics and Software Technologies

# DaSH Research Groups

**Applied Physics**The Applied Physics (AP) Group researches physics-based approaches to aerospace system health management, non-destructive evaluation, and diagnostics & prognostics, with the goal of minimizing design risk and enhancing operational safety of aircraft and spacecraft. Current efforts are focused on multi-physics modeling and optimization of additive manufacturing processes for metals, plastics, and composites.

**Computational Materials**The Computational Materials (CM) Group provides expertise in multiscale modeling, theory and simulation of materials, devices, and biosystems. Our goal is to develop new materials and biosystems for NASA’s high-performance applications. We participate in multi-disciplinary teams (chemists, physicists, material scientists, engineers) working on both experimental and computational issues. A combination of fundamental modeling, computational high-throughput screening and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges.

**Data Sciences**The Data Sciences (DS) Group collaborates with domain scientists in the fields of Integrated System Health Management, Aeronautics, Space Exploration, Earth Sciences, and Space Sciences to answer pressing scientific questions in the field of machine learning, knowledge discovery, and related areas. We also do fundamental research to create tools and methods to aid in the assimilation and understanding of scientific and engineering data to best advance NASA’s missions.

**Diagnostics & Prognostics**The Diagnostics and Prognostics (DnP) Group focuses on: 1) Developing and maturing diagnostic and prognostic algorithms and uncertainty management techniques; 2) Leading the health management community in defining, standardizing, and applying metrics for measuring Integrated Vehicle Health Management (IVHM) algorithm performance; 3) Investigating component fault behavior and damage progression; and 4) Generating and disseminating data sets for validation of diagnostic and prognostic techniques. The DnP Group maintains a lab with several Hardware-In-the-Loop (HIL) testbeds that are considered a national asset.

**Integrated Systems Health Management (ISHM) Technology Insertion Projects**The ISHM Technology Insertion Projects Group works on the application and maturation of health management technologies for ISHM design and systems engineering, as well as automated model generation, fault management, advanced caution and warning for crew and ground operations, and automated diagnosis for flight and ground operations.

**Quantum Artificial Intelligence Laboratory**The Quantum Artificial Intelligence Laboratory (QuAIL) conducts theoretical and empirical analyses of quantum computing and annealing algorithms, and investigates their applications to challenging computational problems in areas such as Earth-science data understanding, air-traffic flow control, and autonomous decision-making.