Research Overview
The primary aim of the RAISE Initiative is to take a first step in building a coordination network within the Texas A&M community at the intersection among (1) foundational AI research, (2) AI for science, and (3) AI for engineering. Our current specific focuses are:
Foundational AI Research
Machine learning, foundation models, large language models, planning and reasoning agents, generative artificial intelligence, geometric deep learning, reinforcement learning, Bayesian learning and optimization, error estimation and uncertainty quantification, computer vision and multi-modality models, visualization and human-AI interactions, high-performance computing and AI systems.
AI for Science
Quantum physics, quantum chemistry, quantum materials, density functional theory, atomistic system modeling, molecular simulation, dynamics, and interactions, protein modeling, drug discovery, materials discovery, physics informed learning and simulation, mathematical and statistical modeling, ordinary and partial differential equations.
AI for Engineering
Computational fluid dynamics, aerodynamics, combustion, propulsion, detonation, computational gas dynamics, turbulence, hypersonic and reactive flow simulation, aerothermochemistry, extreme environment materials, nuclear system modeling and simulation, hydraulic, hydrodynamics, subsurface, and reservoir modeling, multi-scale and multi-physics modeling and simulation, semiconductor materials and chip design.