Foundational AI
Infusing physics principles into AI to create state-of-the-art AI innovations, including:
- representation learning,
- robust and interpretable AI, and
- reinforcement learning.
Projects in Foundational AI are infusing physics principles into AI to create state-of-the-art AI innovations, particularly in terms of representation learning, robust/interpretable AI, and reinforcement learning. Overall, the goal for projects in this domain is to develop AI techniques that can be used across a variety of applications and that are influenced by physics principles and/or problems. Researchers are tackling AI questions dealing with robustness, interpretability, computer vision, equivariance, and segmentation, among others.
Research Highlights
Theoretical Physics
Research in Physics Theory is leveraging AI to understand the theoretical underpinning of fundamental physics, including:
- nuclear/particle physics,
- quantum field theory and string theory, and
- quantum many-body physics.
Projects in AI for Theoretical Physics utilize AI tools and techniques to enable physics discovery through the acceleration of theoretical physics calculations. In turn, the advances made in this domain also contribute to advancing AI—researchers are not simply using existing AI tools and techniques as they are, but are building on those tools and developing new tools, which can have applications beyond AI. Researchers working in this domain are developing AI to solve problems related to the detection of subhalos, quantum many-body physics, simulation-based inference, lattice quantum field theory, dark matter searches, and knot theory, to name a few.
Research Highlights
Experimental Physics
Enhancing the operations and analysis of flagship NSF experiments through AI, including the:
- Large Hadron Collider,
- IceCube Neutrino Observatory, and
- Laser Interferometer Gravitational Wave Observatory.
The impacts of IAIFI on many experiments aim to exploit AI developments to enhance the quality of physics that can be performed. Data from physics experiments can significantly benefit from the application of AI algorithms leading to better physics measurements and, ultimately, a deeper understanding of the universe. The use of experimental data additionally provides a real-world, often noisy, setting to verify AI methods. Our efforts in AI for experiment are symbiotic with our work to advance the field of AI itself and allow for the rapid deployment of novel ideas to core physics measurements.
Research Highlights
Astrophysics
Using AI techniques to understand the universe on cosmological scales, including:
- dark matter searches,
- structure formation, and
- multi-messenger astrophysics.
The research in Astrophysics is contributing to a variety of subfields, including dark matter searches, large-scale structure of the universe, and galaxy formation. The use of AI is becoming increasingly pervasive in astrophysics, and IAIFI researchers are at the cutting edge of developing techniques for applications ranging from image classification to data interpretation to anomaly detection.